inla.models {INLA}

R Documentation

Valid models in INLA

Description

This page describe the models implemented in inla, divided into sections: latent, group, scopy, mix, link, predictor, hazard, likelihood, prior, wrapper, lp.scale.

Usage

inla.models()

Value

Valid sections are: latent, group, scopy, mix, link, predictor, hazard, likelihood, prior, wrapper, lp.scale.

'latent'

Valid models in this section are:

Model 'linear'.

Properties:

doc =

'Alternative interface to an fixed effect'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'linear'

Number of hyperparmeters is 0.

Model 'iid'.

Properties:

doc =

'Gaussian random effects in dim=1'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'indep'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

1001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'mec'.

Properties:

doc =

'Classical measurement error model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'mec'

Number of hyperparmeters is 4.

Hyperparameter 'theta1'

hyperid =

2001

name =

beta

short.name =

b

prior =

gaussian

param =

1 0.001

initial =

1

fixed =

FALSE

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

2002

name =

prec.u

short.name =

prec

prior =

loggamma

param =

1 1e-04

initial =

9.21034037197618

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

2003

name =

mean.x

short.name =

mu.x

prior =

gaussian

param =

0 1e-04

initial =

0

fixed =

TRUE

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

2004

name =

prec.x

short.name =

prec.x

prior =

loggamma

param =

1 10000

initial =

-9.21034037197618

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'meb'.

Properties:

doc =

'Berkson measurement error model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'meb'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

3001

name =

beta

short.name =

b

prior =

gaussian

param =

1 0.001

initial =

1

fixed =

FALSE

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

3002

name =

prec.u

short.name =

prec

prior =

loggamma

param =

1 1e-04

initial =

6.90775527898214

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rgeneric'.

Properties:

doc =

'Generic latent model specified using R'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'rgeneric'

Number of hyperparmeters is 0.

Model 'cgeneric'.

Properties:

doc =

'Generic latent model specified using C'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'rgeneric'

Number of hyperparmeters is 0.

Model 'rw1'.

Properties:

doc =

'Random walk of order 1'

constr =

'TRUE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

min.diff =

'1e-05'

pdf =

'rw1'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

4001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2'.

Properties:

doc =

'Random walk of order 2'

constr =

'TRUE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

min.diff =

'0.001'

pdf =

'rw2'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

5001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'crw2'.

Properties:

doc =

'Exact solution to the random walk of order 2'

constr =

'TRUE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'2'

aug.constr =

'1'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

min.diff =

'0.001'

pdf =

'crw2'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

6001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'seasonal'.

Properties:

doc =

'Seasonal model for time series'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'seasonal'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

7001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'besag'.

Properties:

doc =

'The Besag area model (CAR-model)'

constr =

'TRUE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'besag'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

8001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'besag2'.

Properties:

doc =

'The shared Besag model'

constr =

'TRUE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'1 2'

n.div.by =

'2'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'besag2'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

9001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

9002

name =

scaling parameter

short.name =

a

prior =

loggamma

param =

10 10

initial =

0

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'bym'.

Properties:

doc =

'The BYM-model (Besag-York-Mollier model)'

constr =

'TRUE'

nrow.ncol =

'FALSE'

augmented =

'TRUE'

aug.factor =

'2'

aug.constr =

'2'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'bym'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

10001

name =

log unstructured precision

short.name =

prec.unstruct

prior =

loggamma

param =

1 5e-04

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

10002

name =

log spatial precision

short.name =

prec.spatial

prior =

loggamma

param =

1 5e-04

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'bym2'.

Properties:

doc =

'The BYM-model with the PC priors'

constr =

'TRUE'

nrow.ncol =

'FALSE'

augmented =

'TRUE'

aug.factor =

'2'

aug.constr =

'2'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'bym2'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

11001

name =

log precision

short.name =

prec

prior =

pc.prec

param =

1 0.01

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

11002

name =

logit phi

short.name =

phi

prior =

pc

param =

0.5 0.5

initial =

-3

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'besagproper'.

Properties:

doc =

'A proper version of the Besag model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'besagproper'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

12001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-04

initial =

2

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

12002

name =

log diagonal

short.name =

diag

prior =

loggamma

param =

1 1

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'besagproper2'.

Properties:

doc =

'An alternative proper version of the Besag model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'besagproper2'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

13001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-04

initial =

2

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

13002

name =

logit lambda

short.name =

lambda

prior =

gaussian

param =

0 0.45

initial =

3

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'fgn'.

Properties:

doc =

'Fractional Gaussian noise model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'TRUE'

aug.factor =

'5'

aug.constr =

'1'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'TRUE'

order.default =

'4'

order.defined =

'3 4'

pdf =

'fgn'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

13101

name =

log precision

short.name =

prec

prior =

pc.prec

param =

3 0.01

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

13102

name =

logit H

short.name =

H

prior =

pcfgnh

param =

0.9 0.1

initial =

2

fixed =

FALSE

to.theta =

function(x) log((2 * x - 1) / (2 * (1 - x)))

from.theta =

function(x) 0.5 + 0.5 * exp(x) / (1 + exp(x))

Model 'fgn2'.

Properties:

doc =

'Fractional Gaussian noise model (alt 2)'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'TRUE'

aug.factor =

'4'

aug.constr =

'1'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'TRUE'

order.default =

'4'

order.defined =

'3 4'

pdf =

'fgn'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

13111

name =

log precision

short.name =

prec

prior =

pc.prec

param =

3 0.01

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

13112

name =

logit H

short.name =

H

prior =

pcfgnh

param =

0.9 0.1

initial =

2

fixed =

FALSE

to.theta =

function(x) log((2 * x - 1) / (2 * (1 - x)))

from.theta =

function(x) 0.5 + 0.5 * exp(x) / (1 + exp(x))

Model 'ar1'.

Properties:

doc =

'Auto-regressive model of order 1 (AR(1))'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'ar1'

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

14001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

14002

name =

logit lag one correlation

short.name =

rho

prior =

normal

param =

0 0.15

initial =

2

fixed =

FALSE

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta3'

hyperid =

14003

name =

mean

short.name =

mean

prior =

normal

param =

0 1

initial =

0

fixed =

TRUE

to.theta =

function(x) x

from.theta =

function(x) x

Model 'ar1c'.

Properties:

doc =

'Auto-regressive model of order 1 w/covariates'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'ar1c'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

14101

name =

log precision

short.name =

prec

prior =

pc.prec

param =

1 0.01

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

14102

name =

logit lag one correlation

short.name =

rho

prior =

pc.cor0

param =

0.5 0.5

initial =

2

fixed =

FALSE

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'ar'.

Properties:

doc =

'Auto-regressive model of order p (AR(p))'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'ar'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

15001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

pc.prec

param =

3 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

15002

name =

pacf1

short.name =

pacf1

initial =

1

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.5

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta3'

hyperid =

15003

name =

pacf2

short.name =

pacf2

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.4

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta4'

hyperid =

15004

name =

pacf3

short.name =

pacf3

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.3

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta5'

hyperid =

15005

name =

pacf4

short.name =

pacf4

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.2

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta6'

hyperid =

15006

name =

pacf5

short.name =

pacf5

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta7'

hyperid =

15007

name =

pacf6

short.name =

pacf6

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta8'

hyperid =

15008

name =

pacf7

short.name =

pacf7

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta9'

hyperid =

15009

name =

pacf8

short.name =

pacf8

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta10'

hyperid =

15010

name =

pacf9

short.name =

pacf9

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta11'

hyperid =

15011

name =

pacf10

short.name =

pacf10

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'ou'.

Properties:

doc =

'The Ornstein-Uhlenbeck process'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'ou'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

16001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

16002

name =

log phi

short.name =

phi

prior =

normal

param =

0 0.2

initial =

-1

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'intslope'.

Properties:

doc =

'Intecept-slope model with Wishart-prior'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'intslope'

Number of hyperparmeters is 13.

Hyperparameter 'theta1'

hyperid =

16101

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart2d

param =

4 1 1 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

16102

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

16103

name =

logit correlation

short.name =

cor

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta4'

hyperid =

16104

name =

gamma1

short.name =

g1

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

16105

name =

gamma2

short.name =

g2

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

16106

name =

gamma3

short.name =

g3

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

16107

name =

gamma4

short.name =

g4

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

16108

name =

gamma5

short.name =

g5

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

16109

name =

gamma6

short.name =

g6

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

16110

name =

gamma7

short.name =

g7

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

16111

name =

gamma8

short.name =

g8

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

16112

name =

gamma9

short.name =

g9

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

16113

name =

gamma10

short.name =

g10

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Model 'generic'.

Properties:

doc =

'A generic model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'generic0'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

17001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'generic0'.

Properties:

doc =

'A generic model (type 0)'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'generic0'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

18001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'generic1'.

Properties:

doc =

'A generic model (type 1)'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'generic1'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

19001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

19002

name =

beta

short.name =

beta

initial =

2

fixed =

FALSE

prior =

gaussian

param =

0 0.1

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'generic2'.

Properties:

doc =

'A generic model (type 2)'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'2'

aug.constr =

'2'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'generic2'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

20001

name =

log precision cmatrix

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

20002

name =

log precision random

short.name =

prec.random

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 0.001

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'generic3'.

Properties:

doc =

'A generic model (type 3)'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'generic3'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

21001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

21002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

21003

name =

log precision3

short.name =

prec3

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

21004

name =

log precision4

short.name =

prec4

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

21005

name =

log precision5

short.name =

prec5

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

21006

name =

log precision6

short.name =

prec6

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta7'

hyperid =

21007

name =

log precision7

short.name =

prec7

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta8'

hyperid =

21008

name =

log precision8

short.name =

prec8

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta9'

hyperid =

21009

name =

log precision9

short.name =

prec9

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta10'

hyperid =

21010

name =

log precision10

short.name =

prec10

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta11'

hyperid =

21011

name =

log precision common

short.name =

prec.common

initial =

0

fixed =

TRUE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'spde'.

Properties:

doc =

'A SPDE model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'spde'

Number of hyperparmeters is 4.

Hyperparameter 'theta1'

hyperid =

22001

name =

theta.T

short.name =

T

initial =

2

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

22002

name =

theta.K

short.name =

K

initial =

-2

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

22003

name =

theta.KT

short.name =

KT

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

22004

name =

theta.OC

short.name =

OC

initial =

-20

fixed =

TRUE

prior =

normal

param =

0 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'spde2'.

Properties:

doc =

'A SPDE2 model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'spde2'

Number of hyperparmeters is 100.

Hyperparameter 'theta1'

hyperid =

23001

name =

theta1

short.name =

t1

initial =

0

fixed =

FALSE

prior =

mvnorm

param =

1 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

23002

name =

theta2

short.name =

t2

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

23003

name =

theta3

short.name =

t3

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

23004

name =

theta4

short.name =

t4

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

23005

name =

theta5

short.name =

t5

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

23006

name =

theta6

short.name =

t6

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

23007

name =

theta7

short.name =

t7

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

23008

name =

theta8

short.name =

t8

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

23009

name =

theta9

short.name =

t9

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

23010

name =

theta10

short.name =

t10

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

23011

name =

theta11

short.name =

t11

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

23012

name =

theta12

short.name =

t12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

23013

name =

theta13

short.name =

t13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

23014

name =

theta14

short.name =

t14

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

23015

name =

theta15

short.name =

t15

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

23016

name =

theta16

short.name =

t16

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

23017

name =

theta17

short.name =

t17

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

23018

name =

theta18

short.name =

t18

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

23019

name =

theta19

short.name =

t19

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

23020

name =

theta20

short.name =

t20

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

23021

name =

theta21

short.name =

t21

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

23022

name =

theta22

short.name =

t22

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

23023

name =

theta23

short.name =

t23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

23024

name =

theta24

short.name =

t24

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

23025

name =

theta25

short.name =

t25

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

23026

name =

theta26

short.name =

t26

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

23027

name =

theta27

short.name =

t27

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

23028

name =

theta28

short.name =

t28

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

23029

name =

theta29

short.name =

t29

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

23030

name =

theta30

short.name =

t30

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

23031

name =

theta31

short.name =

t31

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

23032

name =

theta32

short.name =

t32

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

23033

name =

theta33

short.name =

t33

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

23034

name =

theta34

short.name =

t34

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

23035

name =

theta35

short.name =

t35

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

23036

name =

theta36

short.name =

t36

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

23037

name =

theta37

short.name =

t37

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

23038

name =

theta38

short.name =

t38

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

23039

name =

theta39

short.name =

t39

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

23040

name =

theta40

short.name =

t40

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

23041

name =

theta41

short.name =

t41

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

23042

name =

theta42

short.name =

t42

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

23043

name =

theta43

short.name =

t43

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

23044

name =

theta44

short.name =

t44

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

23045

name =

theta45

short.name =

t45

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

23046

name =

theta46

short.name =

t46

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

23047

name =

theta47

short.name =

t47

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

23048

name =

theta48

short.name =

t48

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

23049

name =

theta49

short.name =

t49

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

23050

name =

theta50

short.name =

t50

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

23051

name =

theta51

short.name =

t51

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta52'

hyperid =

23052

name =

theta52

short.name =

t52

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta53'

hyperid =

23053

name =

theta53

short.name =

t53

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta54'

hyperid =

23054

name =

theta54

short.name =

t54

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta55'

hyperid =

23055

name =

theta55

short.name =

t55

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta56'

hyperid =

23056

name =

theta56

short.name =

t56

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta57'

hyperid =

23057

name =

theta57

short.name =

t57

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta58'

hyperid =

23058

name =

theta58

short.name =

t58

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta59'

hyperid =

23059

name =

theta59

short.name =

t59

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta60'

hyperid =

23060

name =

theta60

short.name =

t60

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta61'

hyperid =

23061

name =

theta61

short.name =

t61

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta62'

hyperid =

23062

name =

theta62

short.name =

t62

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta63'

hyperid =

23063

name =

theta63

short.name =

t63

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta64'

hyperid =

23064

name =

theta64

short.name =

t64

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta65'

hyperid =

23065

name =

theta65

short.name =

t65

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta66'

hyperid =

23066

name =

theta66

short.name =

t66

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta67'

hyperid =

23067

name =

theta67

short.name =

t67

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta68'

hyperid =

23068

name =

theta68

short.name =

t68

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta69'

hyperid =

23069

name =

theta69

short.name =

t69

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta70'

hyperid =

23070

name =

theta70

short.name =

t70

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta71'

hyperid =

23071

name =

theta71

short.name =

t71

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta72'

hyperid =

23072

name =

theta72

short.name =

t72

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta73'

hyperid =

23073

name =

theta73

short.name =

t73

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta74'

hyperid =

23074

name =

theta74

short.name =

t74

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta75'

hyperid =

23075

name =

theta75

short.name =

t75

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta76'

hyperid =

23076

name =

theta76

short.name =

t76

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta77'

hyperid =

23077

name =

theta77

short.name =

t77

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta78'

hyperid =

23078

name =

theta78

short.name =

t78

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta79'

hyperid =

23079

name =

theta79

short.name =

t79

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta80'

hyperid =

23080

name =

theta80

short.name =

t80

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta81'

hyperid =

23081

name =

theta81

short.name =

t81

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta82'

hyperid =

23082

name =

theta82

short.name =

t82

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta83'

hyperid =

23083

name =

theta83

short.name =

t83

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta84'

hyperid =

23084

name =

theta84

short.name =

t84

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta85'

hyperid =

23085

name =

theta85

short.name =

t85

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta86'

hyperid =

23086

name =

theta86

short.name =

t86

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta87'

hyperid =

23087

name =

theta87

short.name =

t87

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta88'

hyperid =

23088

name =

theta88

short.name =

t88

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta89'

hyperid =

23089

name =

theta89

short.name =

t89

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta90'

hyperid =

23090

name =

theta90

short.name =

t90

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta91'

hyperid =

23091

name =

theta91

short.name =

t91

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta92'

hyperid =

23092

name =

theta92

short.name =

t92

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta93'

hyperid =

23093

name =

theta93

short.name =

t93

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta94'

hyperid =

23094

name =

theta94

short.name =

t94

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta95'

hyperid =

23095

name =

theta95

short.name =

t95

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta96'

hyperid =

23096

name =

theta96

short.name =

t96

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta97'

hyperid =

23097

name =

theta97

short.name =

t97

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta98'

hyperid =

23098

name =

theta98

short.name =

t98

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta99'

hyperid =

23099

name =

theta99

short.name =

t99

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta100'

hyperid =

23100

name =

theta100

short.name =

t100

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model 'spde3'.

Properties:

doc =

'A SPDE3 model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'spde3'

Number of hyperparmeters is 100.

Hyperparameter 'theta1'

hyperid =

24001

name =

theta1

short.name =

t1

initial =

0

fixed =

FALSE

prior =

mvnorm

param =

1 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

24002

name =

theta2

short.name =

t2

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

24003

name =

theta3

short.name =

t3

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

24004

name =

theta4

short.name =

t4

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

24005

name =

theta5

short.name =

t5

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

24006

name =

theta6

short.name =

t6

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

24007

name =

theta7

short.name =

t7

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

24008

name =

theta8

short.name =

t8

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

24009

name =

theta9

short.name =

t9

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

24010

name =

theta10

short.name =

t10

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

24011

name =

theta11

short.name =

t11

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

24012

name =

theta12

short.name =

t12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

24013

name =

theta13

short.name =

t13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

24014

name =

theta14

short.name =

t14

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

24015

name =

theta15

short.name =

t15

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

24016

name =

theta16

short.name =

t16

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

24017

name =

theta17

short.name =

t17

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

24018

name =

theta18

short.name =

t18

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

24019

name =

theta19

short.name =

t19

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

24020

name =

theta20

short.name =

t20

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

24021

name =

theta21

short.name =

t21

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

24022

name =

theta22

short.name =

t22

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

24023

name =

theta23

short.name =

t23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

24024

name =

theta24

short.name =

t24

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

24025

name =

theta25

short.name =

t25

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

24026

name =

theta26

short.name =

t26

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

24027

name =

theta27

short.name =

t27

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

24028

name =

theta28

short.name =

t28

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

24029

name =

theta29

short.name =

t29

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

24030

name =

theta30

short.name =

t30

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

24031

name =

theta31

short.name =

t31

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

24032

name =

theta32

short.name =

t32

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

24033

name =

theta33

short.name =

t33

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

24034

name =

theta34

short.name =

t34

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

24035

name =

theta35

short.name =

t35

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

24036

name =

theta36

short.name =

t36

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

24037

name =

theta37

short.name =

t37

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

24038

name =

theta38

short.name =

t38

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

24039

name =

theta39

short.name =

t39

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

24040

name =

theta40

short.name =

t40

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

24041

name =

theta41

short.name =

t41

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

24042

name =

theta42

short.name =

t42

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

24043

name =

theta43

short.name =

t43

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

24044

name =

theta44

short.name =

t44

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

24045

name =

theta45

short.name =

t45

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

24046

name =

theta46

short.name =

t46

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

24047

name =

theta47

short.name =

t47

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

24048

name =

theta48

short.name =

t48

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

24049

name =

theta49

short.name =

t49

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

24050

name =

theta50

short.name =

t50

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

24051

name =

theta51

short.name =

t51

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta52'

hyperid =

24052

name =

theta52

short.name =

t52

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta53'

hyperid =

24053

name =

theta53

short.name =

t53

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta54'

hyperid =

24054

name =

theta54

short.name =

t54

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta55'

hyperid =

24055

name =

theta55

short.name =

t55

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta56'

hyperid =

24056

name =

theta56

short.name =

t56

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta57'

hyperid =

24057

name =

theta57

short.name =

t57

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta58'

hyperid =

24058

name =

theta58

short.name =

t58

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta59'

hyperid =

24059

name =

theta59

short.name =

t59

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta60'

hyperid =

24060

name =

theta60

short.name =

t60

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta61'

hyperid =

24061

name =

theta61

short.name =

t61

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta62'

hyperid =

24062

name =

theta62

short.name =

t62

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta63'

hyperid =

24063

name =

theta63

short.name =

t63

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta64'

hyperid =

24064

name =

theta64

short.name =

t64

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta65'

hyperid =

24065

name =

theta65

short.name =

t65

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta66'

hyperid =

24066

name =

theta66

short.name =

t66

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta67'

hyperid =

24067

name =

theta67

short.name =

t67

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta68'

hyperid =

24068

name =

theta68

short.name =

t68

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta69'

hyperid =

24069

name =

theta69

short.name =

t69

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta70'

hyperid =

24070

name =

theta70

short.name =

t70

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta71'

hyperid =

24071

name =

theta71

short.name =

t71

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta72'

hyperid =

24072

name =

theta72

short.name =

t72

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta73'

hyperid =

24073

name =

theta73

short.name =

t73

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta74'

hyperid =

24074

name =

theta74

short.name =

t74

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta75'

hyperid =

24075

name =

theta75

short.name =

t75

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta76'

hyperid =

24076

name =

theta76

short.name =

t76

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta77'

hyperid =

24077

name =

theta77

short.name =

t77

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta78'

hyperid =

24078

name =

theta78

short.name =

t78

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta79'

hyperid =

24079

name =

theta79

short.name =

t79

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta80'

hyperid =

24080

name =

theta80

short.name =

t80

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta81'

hyperid =

24081

name =

theta81

short.name =

t81

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta82'

hyperid =

24082

name =

theta82

short.name =

t82

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta83'

hyperid =

24083

name =

theta83

short.name =

t83

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta84'

hyperid =

24084

name =

theta84

short.name =

t84

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta85'

hyperid =

24085

name =

theta85

short.name =

t85

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta86'

hyperid =

24086

name =

theta86

short.name =

t86

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta87'

hyperid =

24087

name =

theta87

short.name =

t87

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta88'

hyperid =

24088

name =

theta88

short.name =

t88

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta89'

hyperid =

24089

name =

theta89

short.name =

t89

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta90'

hyperid =

24090

name =

theta90

short.name =

t90

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta91'

hyperid =

24091

name =

theta91

short.name =

t91

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta92'

hyperid =

24092

name =

theta92

short.name =

t92

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta93'

hyperid =

24093

name =

theta93

short.name =

t93

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta94'

hyperid =

24094

name =

theta94

short.name =

t94

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta95'

hyperid =

24095

name =

theta95

short.name =

t95

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta96'

hyperid =

24096

name =

theta96

short.name =

t96

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta97'

hyperid =

24097

name =

theta97

short.name =

t97

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta98'

hyperid =

24098

name =

theta98

short.name =

t98

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta99'

hyperid =

24099

name =

theta99

short.name =

t99

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta100'

hyperid =

24100

name =

theta100

short.name =

t100

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model 'iid1d'.

Properties:

doc =

'Gaussian random effect in dim=1 with Wishart prior'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'TRUE'

pdf =

'iid123d'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

25001

name =

precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

wishart1d

param =

2 1e-04

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'iid2d'.

Properties:

doc =

'Gaussian random effect in dim=2 with Wishart prior'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'TRUE'

aug.factor =

'1'

aug.constr =

'1 2'

n.div.by =

'2'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'iid123d'

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

26001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart2d

param =

4 1 1 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

26002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

26003

name =

logit correlation

short.name =

cor

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'iid3d'.

Properties:

doc =

'Gaussian random effect in dim=3 with Wishart prior'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'TRUE'

aug.factor =

'1'

aug.constr =

'1 2 3'

n.div.by =

'3'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'iid123d'

Number of hyperparmeters is 6.

Hyperparameter 'theta1'

hyperid =

27001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart3d

param =

7 1 1 1 0 0 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

27002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

27003

name =

log precision3

short.name =

prec3

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

27004

name =

logit correlation12

short.name =

cor12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta5'

hyperid =

27005

name =

logit correlation13

short.name =

cor13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta6'

hyperid =

27006

name =

logit correlation23

short.name =

cor23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'iid4d'.

Properties:

doc =

'Gaussian random effect in dim=4 with Wishart prior'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'TRUE'

aug.factor =

'1'

aug.constr =

'1 2 3 4'

n.div.by =

'4'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'iid123d'

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

28001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart4d

param =

11 1 1 1 1 0 0 0 0 0 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

28002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

28003

name =

log precision3

short.name =

prec3

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

28004

name =

log precision4

short.name =

prec4

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

28005

name =

logit correlation12

short.name =

cor12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta6'

hyperid =

28006

name =

logit correlation13

short.name =

cor13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta7'

hyperid =

28007

name =

logit correlation14

short.name =

cor14

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta8'

hyperid =

28008

name =

logit correlation23

short.name =

cor23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta9'

hyperid =

28009

name =

logit correlation24

short.name =

cor24

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta10'

hyperid =

28010

name =

logit correlation34

short.name =

cor34

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'iid5d'.

Properties:

doc =

'Gaussian random effect in dim=5 with Wishart prior'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'TRUE'

aug.factor =

'1'

aug.constr =

'1 2 3 4 5'

n.div.by =

'5'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'iid123d'

Number of hyperparmeters is 15.

Hyperparameter 'theta1'

hyperid =

29001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart5d

param =

16 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

29002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

29003

name =

log precision3

short.name =

prec3

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

29004

name =

log precision4

short.name =

prec4

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

29005

name =

log precision5

short.name =

prec5

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

29006

name =

logit correlation12

short.name =

cor12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta7'

hyperid =

29007

name =

logit correlation13

short.name =

cor13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta8'

hyperid =

29008

name =

logit correlation14

short.name =

cor14

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta9'

hyperid =

29009

name =

logit correlation15

short.name =

cor15

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta10'

hyperid =

29010

name =

logit correlation23

short.name =

cor23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta11'

hyperid =

29011

name =

logit correlation24

short.name =

cor24

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta12'

hyperid =

29012

name =

logit correlation25

short.name =

cor25

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta13'

hyperid =

29013

name =

logit correlation34

short.name =

cor34

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta14'

hyperid =

29014

name =

logit correlation35

short.name =

cor35

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta15'

hyperid =

29015

name =

logit correlation45

short.name =

cor45

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'iidkd'.

Properties:

doc =

'Gaussian random effect in dim=k with Wishart prior'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'TRUE'

aug.factor =

'1'

aug.constr =

'1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20'

n.div.by =

'-1'

n.required =

'TRUE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'iidkd'

Number of hyperparmeters is 210.

Hyperparameter 'theta1'

hyperid =

29101

name =

theta1

short.name =

theta1

initial =

1048576

fixed =

FALSE

prior =

wishartkd

param =

21 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

29102

name =

theta2

short.name =

theta2

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

29103

name =

theta3

short.name =

theta3

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

29104

name =

theta4

short.name =

theta4

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

29105

name =

theta5

short.name =

theta5

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

29106

name =

theta6

short.name =

theta6

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

29107

name =

theta7

short.name =

theta7

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

29108

name =

theta8

short.name =

theta8

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

29109

name =

theta9

short.name =

theta9

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

29110

name =

theta10

short.name =

theta10

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

29111

name =

theta11

short.name =

theta11

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

29112

name =

theta12

short.name =

theta12

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

29113

name =

theta13

short.name =

theta13

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

29114

name =

theta14

short.name =

theta14

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

29115

name =

theta15

short.name =

theta15

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

29116

name =

theta16

short.name =

theta16

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

29117

name =

theta17

short.name =

theta17

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

29118

name =

theta18

short.name =

theta18

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

29119

name =

theta19

short.name =

theta19

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

29120

name =

theta20

short.name =

theta20

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

29121

name =

theta21

short.name =

theta21

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

29122

name =

theta22

short.name =

theta22

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

29123

name =

theta23

short.name =

theta23

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

29124

name =

theta24

short.name =

theta24

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

29125

name =

theta25

short.name =

theta25

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

29126

name =

theta26

short.name =

theta26

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

29127

name =

theta27

short.name =

theta27

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

29128

name =

theta28

short.name =

theta28

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

29129

name =

theta29

short.name =

theta29

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

29130

name =

theta30

short.name =

theta30

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

29131

name =

theta31

short.name =

theta31

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

29132

name =

theta32

short.name =

theta32

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

29133

name =

theta33

short.name =

theta33

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

29134

name =

theta34

short.name =

theta34

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

29135

name =

theta35

short.name =

theta35

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

29136

name =

theta36

short.name =

theta36

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

29137

name =

theta37

short.name =

theta37

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

29138

name =

theta38

short.name =

theta38

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

29139

name =

theta39

short.name =

theta39

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

29140

name =

theta40

short.name =

theta40

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

29141

name =

theta41

short.name =

theta41

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

29142

name =

theta42

short.name =

theta42

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

29143

name =

theta43

short.name =

theta43

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

29144

name =

theta44

short.name =

theta44

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

29145

name =

theta45

short.name =

theta45

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

29146

name =

theta46

short.name =

theta46

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

29147

name =

theta47

short.name =

theta47

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

29148

name =

theta48

short.name =

theta48

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

29149

name =

theta49

short.name =

theta49

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

29150

name =

theta50

short.name =

theta50

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

29151

name =

theta51

short.name =

theta51

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta52'

hyperid =

29152

name =

theta52

short.name =

theta52

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta53'

hyperid =

29153

name =

theta53

short.name =

theta53

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta54'

hyperid =

29154

name =

theta54

short.name =

theta54

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta55'

hyperid =

29155

name =

theta55

short.name =

theta55

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta56'

hyperid =

29156

name =

theta56

short.name =

theta56

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta57'

hyperid =

29157

name =

theta57

short.name =

theta57

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta58'

hyperid =

29158

name =

theta58

short.name =

theta58

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta59'

hyperid =

29159

name =

theta59

short.name =

theta59

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta60'

hyperid =

29160

name =

theta60

short.name =

theta60

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta61'

hyperid =

29161

name =

theta61

short.name =

theta61

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta62'

hyperid =

29162

name =

theta62

short.name =

theta62

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta63'

hyperid =

29163

name =

theta63

short.name =

theta63

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta64'

hyperid =

29164

name =

theta64

short.name =

theta64

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta65'

hyperid =

29165

name =

theta65

short.name =

theta65

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta66'

hyperid =

29166

name =

theta66

short.name =

theta66

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta67'

hyperid =

29167

name =

theta67

short.name =

theta67

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta68'

hyperid =

29168

name =

theta68

short.name =

theta68

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta69'

hyperid =

29169

name =

theta69

short.name =

theta69

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta70'

hyperid =

29170

name =

theta70

short.name =

theta70

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta71'

hyperid =

29171

name =

theta71

short.name =

theta71

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta72'

hyperid =

29172

name =

theta72

short.name =

theta72

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta73'

hyperid =

29173

name =

theta73

short.name =

theta73

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta74'

hyperid =

29174

name =

theta74

short.name =

theta74

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta75'

hyperid =

29175

name =

theta75

short.name =

theta75

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta76'

hyperid =

29176

name =

theta76

short.name =

theta76

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta77'

hyperid =

29177

name =

theta77

short.name =

theta77

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta78'

hyperid =

29178

name =

theta78

short.name =

theta78

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta79'

hyperid =

29179

name =

theta79

short.name =

theta79

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta80'

hyperid =

29180

name =

theta80

short.name =

theta80

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta81'

hyperid =

29181

name =

theta81

short.name =

theta81

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta82'

hyperid =

29182

name =

theta82

short.name =

theta82

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta83'

hyperid =

29183

name =

theta83

short.name =

theta83

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta84'

hyperid =

29184

name =

theta84

short.name =

theta84

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta85'

hyperid =

29185

name =

theta85

short.name =

theta85

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta86'

hyperid =

29186

name =

theta86

short.name =

theta86

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta87'

hyperid =

29187

name =

theta87

short.name =

theta87

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta88'

hyperid =

29188

name =

theta88

short.name =

theta88

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta89'

hyperid =

29189

name =

theta89

short.name =

theta89

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta90'

hyperid =

29190

name =

theta90

short.name =

theta90

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta91'

hyperid =

29191

name =

theta91

short.name =

theta91

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta92'

hyperid =

29192

name =

theta92

short.name =

theta92

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta93'

hyperid =

29193

name =

theta93

short.name =

theta93

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta94'

hyperid =

29194

name =

theta94

short.name =

theta94

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta95'

hyperid =

29195

name =

theta95

short.name =

theta95

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta96'

hyperid =

29196

name =

theta96

short.name =

theta96

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta97'

hyperid =

29197

name =

theta97

short.name =

theta97

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta98'

hyperid =

29198

name =

theta98

short.name =

theta98

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta99'

hyperid =

29199

name =

theta99

short.name =

theta99

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta100'

hyperid =

29200

name =

theta100

short.name =

theta100

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta101'

hyperid =

29201

name =

theta101

short.name =

theta101

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta102'

hyperid =

29202

name =

theta102

short.name =

theta102

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta103'

hyperid =

29203

name =

theta103

short.name =

theta103

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta104'

hyperid =

29204

name =

theta104

short.name =

theta104

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta105'

hyperid =

29205

name =

theta105

short.name =

theta105

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta106'

hyperid =

29206

name =

theta106

short.name =

theta106

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta107'

hyperid =

29207

name =

theta107

short.name =

theta107

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta108'

hyperid =

29208

name =

theta108

short.name =

theta108

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta109'

hyperid =

29209

name =

theta109

short.name =

theta109

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta110'

hyperid =

29210

name =

theta110

short.name =

theta110

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta111'

hyperid =

29211

name =

theta111

short.name =

theta111

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta112'

hyperid =

29212

name =

theta112

short.name =

theta112

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta113'

hyperid =

29213

name =

theta113

short.name =

theta113

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta114'

hyperid =

29214

name =

theta114

short.name =

theta114

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta115'

hyperid =

29215

name =

theta115

short.name =

theta115

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta116'

hyperid =

29216

name =

theta116

short.name =

theta116

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta117'

hyperid =

29217

name =

theta117

short.name =

theta117

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta118'

hyperid =

29218

name =

theta118

short.name =

theta118

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta119'

hyperid =

29219

name =

theta119

short.name =

theta119

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta120'

hyperid =

29220

name =

theta120

short.name =

theta120

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta121'

hyperid =

29221

name =

theta121

short.name =

theta121

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta122'

hyperid =

29222

name =

theta122

short.name =

theta122

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta123'

hyperid =

29223

name =

theta123

short.name =

theta123

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta124'

hyperid =

29224

name =

theta124

short.name =

theta124

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta125'

hyperid =

29225

name =

theta125

short.name =

theta125

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta126'

hyperid =

29226

name =

theta126

short.name =

theta126

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta127'

hyperid =

29227

name =

theta127

short.name =

theta127

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta128'

hyperid =

29228

name =

theta128

short.name =

theta128

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta129'

hyperid =

29229

name =

theta129

short.name =

theta129

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta130'

hyperid =

29230

name =

theta130

short.name =

theta130

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta131'

hyperid =

29231

name =

theta131

short.name =

theta131

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta132'

hyperid =

29232

name =

theta132

short.name =

theta132

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta133'

hyperid =

29233

name =

theta133

short.name =

theta133

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta134'

hyperid =

29234

name =

theta134

short.name =

theta134

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta135'

hyperid =

29235

name =

theta135

short.name =

theta135

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta136'

hyperid =

29236

name =

theta136

short.name =

theta136

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta137'

hyperid =

29237

name =

theta137

short.name =

theta137

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta138'

hyperid =

29238

name =

theta138

short.name =

theta138

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta139'

hyperid =

29239

name =

theta139

short.name =

theta139

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta140'

hyperid =

29240

name =

theta140

short.name =

theta140

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta141'

hyperid =

29241

name =

theta141

short.name =

theta141

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta142'

hyperid =

29242

name =

theta142

short.name =

theta142

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta143'

hyperid =

29243

name =

theta143

short.name =

theta143

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta144'

hyperid =

29244

name =

theta144

short.name =

theta144

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta145'

hyperid =

29245

name =

theta145

short.name =

theta145

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta146'

hyperid =

29246

name =

theta146

short.name =

theta146

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta147'

hyperid =

29247

name =

theta147

short.name =

theta147

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta148'

hyperid =

29248

name =

theta148

short.name =

theta148

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta149'

hyperid =

29249

name =

theta149

short.name =

theta149

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta150'

hyperid =

29250

name =

theta150

short.name =

theta150

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta151'

hyperid =

29251

name =

theta151

short.name =

theta151

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta152'

hyperid =

29252

name =

theta152

short.name =

theta152

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta153'

hyperid =

29253

name =

theta153

short.name =

theta153

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta154'

hyperid =

29254

name =

theta154

short.name =

theta154

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta155'

hyperid =

29255

name =

theta155

short.name =

theta155

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta156'

hyperid =

29256

name =

theta156

short.name =

theta156

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta157'

hyperid =

29257

name =

theta157

short.name =

theta157

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta158'

hyperid =

29258

name =

theta158

short.name =

theta158

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta159'

hyperid =

29259

name =

theta159

short.name =

theta159

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta160'

hyperid =

29260

name =

theta160

short.name =

theta160

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta161'

hyperid =

29261

name =

theta161

short.name =

theta161

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta162'

hyperid =

29262

name =

theta162

short.name =

theta162

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta163'

hyperid =

29263

name =

theta163

short.name =

theta163

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta164'

hyperid =

29264

name =

theta164

short.name =

theta164

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta165'

hyperid =

29265

name =

theta165

short.name =

theta165

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta166'

hyperid =

29266

name =

theta166

short.name =

theta166

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta167'

hyperid =

29267

name =

theta167

short.name =

theta167

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta168'

hyperid =

29268

name =

theta168

short.name =

theta168

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta169'

hyperid =

29269

name =

theta169

short.name =

theta169

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta170'

hyperid =

29270

name =

theta170

short.name =

theta170

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta171'

hyperid =

29271

name =

theta171

short.name =

theta171

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta172'

hyperid =

29272

name =

theta172

short.name =

theta172

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta173'

hyperid =

29273

name =

theta173

short.name =

theta173

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta174'

hyperid =

29274

name =

theta174

short.name =

theta174

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta175'

hyperid =

29275

name =

theta175

short.name =

theta175

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta176'

hyperid =

29276

name =

theta176

short.name =

theta176

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta177'

hyperid =

29277

name =

theta177

short.name =

theta177

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta178'

hyperid =

29278

name =

theta178

short.name =

theta178

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta179'

hyperid =

29279

name =

theta179

short.name =

theta179

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta180'

hyperid =

29280

name =

theta180

short.name =

theta180

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta181'

hyperid =

29281

name =

theta181

short.name =

theta181

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta182'

hyperid =

29282

name =

theta182

short.name =

theta182

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta183'

hyperid =

29283

name =

theta183

short.name =

theta183

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta184'

hyperid =

29284

name =

theta184

short.name =

theta184

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta185'

hyperid =

29285

name =

theta185

short.name =

theta185

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta186'

hyperid =

29286

name =

theta186

short.name =

theta186

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta187'

hyperid =

29287

name =

theta187

short.name =

theta187

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta188'

hyperid =

29288

name =

theta188

short.name =

theta188

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta189'

hyperid =

29289

name =

theta189

short.name =

theta189

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta190'

hyperid =

29290

name =

theta190

short.name =

theta190

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta191'

hyperid =

29291

name =

theta191

short.name =

theta191

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta192'

hyperid =

29292

name =

theta192

short.name =

theta192

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta193'

hyperid =

29293

name =

theta193

short.name =

theta193

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta194'

hyperid =

29294

name =

theta194

short.name =

theta194

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta195'

hyperid =

29295

name =

theta195

short.name =

theta195

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta196'

hyperid =

29296

name =

theta196

short.name =

theta196

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta197'

hyperid =

29297

name =

theta197

short.name =

theta197

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta198'

hyperid =

29298

name =

theta198

short.name =

theta198

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta199'

hyperid =

29299

name =

theta199

short.name =

theta199

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta200'

hyperid =

29300

name =

theta200

short.name =

theta200

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta201'

hyperid =

29301

name =

theta201

short.name =

theta201

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta202'

hyperid =

29302

name =

theta202

short.name =

theta202

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta203'

hyperid =

29303

name =

theta203

short.name =

theta203

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta204'

hyperid =

29304

name =

theta204

short.name =

theta204

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta205'

hyperid =

29305

name =

theta205

short.name =

theta205

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta206'

hyperid =

29306

name =

theta206

short.name =

theta206

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta207'

hyperid =

29307

name =

theta207

short.name =

theta207

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta208'

hyperid =

29308

name =

theta208

short.name =

theta208

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta209'

hyperid =

29309

name =

theta209

short.name =

theta209

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta210'

hyperid =

29310

name =

theta210

short.name =

theta210

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model '2diid'.

Properties:

doc =

'(This model is obsolute)'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'1 2'

n.div.by =

'2'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'iid123d'

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

30001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

30002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

30003

name =

correlation

short.name =

cor

initial =

4

fixed =

FALSE

prior =

normal

param =

0 0.15

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'z'.

Properties:

doc =

'The z-model in a classical mixed model formulation'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'z'

status =

'experimental'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

31001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2d'.

Properties:

doc =

'Thin-plate spline model'

constr =

'TRUE'

nrow.ncol =

'TRUE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'TRUE'

pdf =

'rw2d'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

32001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2diid'.

Properties:

doc =

'Thin-plate spline with iid noise'

constr =

'TRUE'

nrow.ncol =

'TRUE'

augmented =

'TRUE'

aug.factor =

'2'

aug.constr =

'2'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'rw2diid'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

33001

name =

log precision

short.name =

prec

prior =

pc.prec

param =

1 0.01

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

33002

name =

logit phi

short.name =

phi

prior =

pc

param =

0.5 0.5

initial =

3

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'slm'.

Properties:

doc =

'Spatial lag model'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

pdf =

'slm'

status =

'experimental'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

34001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

34002

name =

rho

short.name =

rho

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) 1 / (1 + exp(-x))

Model 'matern2d'.

Properties:

doc =

'Matern covariance function on a regular grid'

constr =

'FALSE'

nrow.ncol =

'TRUE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'TRUE'

pdf =

'matern2d'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

35001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

35002

name =

log range

short.name =

range

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'dmatern'.

Properties:

doc =

'Dense Matern field'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'TRUE'

set.default.values =

'TRUE'

status =

'experimental'

pdf =

'dmatern'

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

35101

name =

log precision

short.name =

prec

initial =

3

fixed =

FALSE

prior =

pc.prec

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

35102

name =

log range

short.name =

range

initial =

0

fixed =

FALSE

prior =

pc.range

param =

1 0.5

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

35103

name =

log nu

short.name =

nu

initial =

-0.693147180559945

fixed =

TRUE

prior =

loggamma

param =

0.5 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'copy'.

Properties:

doc =

'Create a copy of a model component'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'copy'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

36001

name =

beta

short.name =

b

initial =

0

fixed =

TRUE

prior =

normal

param =

1 10

to.theta =

⁠function(x, REPLACE.ME.low, REPLACE.ME.high) {⁠ ⁠ if (all(is.infinite(c(low, high))) || low == high) {⁠ return(x) ⁠ } else if (all(is.finite(c(low, high)))) {⁠ stopifnot(low < high) return(log(-(low - x) / (high - x))) ⁠ } else if (is.finite(low) && is.infinite(high) && high > low) {⁠ return(log(x - low)) ⁠ } else {⁠ stop("Condition not yet implemented") ⁠ }⁠ ⁠ }⁠

from.theta =

⁠function(x, REPLACE.ME.low, REPLACE.ME.high) {⁠ ⁠ if (all(is.infinite(c(low, high))) || low == high) {⁠ return(x) ⁠ } else if (all(is.finite(c(low, high)))) {⁠ stopifnot(low < high) return(low + exp(x) / (1 + exp(x)) * (high - low)) ⁠ } else if (is.finite(low) && is.infinite(high) && high > low) {⁠ return(low + exp(x)) ⁠ } else {⁠ stop("Condition not yet implemented") ⁠ }⁠ ⁠ }⁠

Model 'scopy'.

Properties:

doc =

'Create a scopy of a model component'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

status =

'experimental'

pdf =

'scopy'

Number of hyperparmeters is 15.

Hyperparameter 'theta1'

hyperid =

36101

name =

beta1

short.name =

b1

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

36102

name =

beta2

short.name =

b2

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

36103

name =

beta3

short.name =

b3

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

36104

name =

beta4

short.name =

b4

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

36105

name =

beta5

short.name =

b5

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

36106

name =

beta6

short.name =

b6

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

36107

name =

beta7

short.name =

b7

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

36108

name =

beta8

short.name =

b8

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

36109

name =

beta9

short.name =

b9

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

36110

name =

beta10

short.name =

b10

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

36111

name =

beta11

short.name =

b11

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

36112

name =

beta12

short.name =

b12

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

36113

name =

beta13

short.name =

b13

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

36114

name =

beta14

short.name =

b14

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

36115

name =

beta15

short.name =

b15

initial =

0.1

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model 'clinear'.

Properties:

doc =

'Constrained linear effect'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'clinear'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

37001

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

⁠function(x, REPLACE.ME.low, REPLACE.ME.high) {⁠ ⁠ if (all(is.infinite(c(low, high))) || low == high) {⁠ stopifnot(low < high) return(x) ⁠ } else if (all(is.finite(c(low, high)))) {⁠ stopifnot(low < high) return(log(-(low - x) / (high - x))) ⁠ } else if (is.finite(low) && is.infinite(high) && high > low) {⁠ return(log(x - low)) ⁠ } else {⁠ stop("Condition not yet implemented") ⁠ }⁠ ⁠ }⁠

from.theta =

⁠function(x, REPLACE.ME.low, REPLACE.ME.high) {⁠ ⁠ if (all(is.infinite(c(low, high))) || low == high) {⁠ stopifnot(low < high) return(x) ⁠ } else if (all(is.finite(c(low, high)))) {⁠ stopifnot(low < high) return(low + exp(x) / (1 + exp(x)) * (high - low)) ⁠ } else if (is.finite(low) && is.infinite(high) && high > low) {⁠ return(low + exp(x)) ⁠ } else {⁠ stop("Condition not yet implemented") ⁠ }⁠ ⁠ }⁠

Model 'sigm'.

Properties:

doc =

'Sigmoidal effect of a covariate'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

status =

'experimental'

pdf =

'sigm'

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

38001

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

38002

name =

loghalflife

short.name =

halflife

initial =

3

fixed =

FALSE

prior =

loggamma

param =

3 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

38003

name =

logshape

short.name =

shape

initial =

0

fixed =

FALSE

prior =

loggamma

param =

10 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'revsigm'.

Properties:

doc =

'Reverse sigmoidal effect of a covariate'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

status =

'experimental'

pdf =

'sigm'

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

39001

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

39002

name =

loghalflife

short.name =

halflife

initial =

3

fixed =

FALSE

prior =

loggamma

param =

3 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

39003

name =

logshape

short.name =

shape

initial =

0

fixed =

FALSE

prior =

loggamma

param =

10 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'log1exp'.

Properties:

doc =

'A nonlinear model of a covariate'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

status =

'experimental'

pdf =

'log1exp'

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

39011

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

39012

name =

alpha

short.name =

a

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

39013

name =

gamma

short.name =

g

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Model 'logdist'.

Properties:

doc =

'A nonlinear model of a covariate'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

status =

'experimental'

pdf =

'logdist'

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

39021

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

39022

name =

alpha1

short.name =

a1

initial =

0

fixed =

FALSE

prior =

loggamma

param =

0.1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

39023

name =

alpha2

short.name =

a2

initial =

0

fixed =

FALSE

prior =

loggamma

param =

0.1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'group'

Valid models in this section are:

Model 'exchangeable'.

Properties:

doc =

'Exchangeable correlations'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

40001

name =

logit correlation

short.name =

rho

initial =

1

fixed =

FALSE

prior =

normal

param =

0 0.2

to.theta =

function(x, REPLACE.ME.ngroup) log((1 + x * (ngroup - 1)) / (1 - x))

from.theta =

function(x, REPLACE.ME.ngroup) (exp(x) - 1) / (exp(x) + ngroup - 1)

Model 'exchangeablepos'.

Properties:

doc =

'Exchangeable positive correlations'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

40101

name =

logit correlation

short.name =

rho

initial =

1

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.5

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'ar1'.

Properties:

doc =

'AR(1) correlations'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

41001

name =

logit correlation

short.name =

rho

initial =

2

fixed =

FALSE

prior =

normal

param =

0 0.15

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'ar'.

Properties:

doc =

'AR(p) correlations'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

42001

name =

log precision

short.name =

prec

initial =

0

fixed =

TRUE

prior =

pc.prec

param =

3 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

42002

name =

pacf1

short.name =

pacf1

initial =

2

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.5

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta3'

hyperid =

42003

name =

pacf2

short.name =

pacf2

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.4

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta4'

hyperid =

42004

name =

pacf3

short.name =

pacf3

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.3

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta5'

hyperid =

42005

name =

pacf4

short.name =

pacf4

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.2

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta6'

hyperid =

42006

name =

pacf5

short.name =

pacf5

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta7'

hyperid =

42007

name =

pacf6

short.name =

pacf6

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta8'

hyperid =

42008

name =

pacf7

short.name =

pacf7

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta9'

hyperid =

42009

name =

pacf8

short.name =

pacf8

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta10'

hyperid =

42010

name =

pacf9

short.name =

pacf9

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta11'

hyperid =

42011

name =

pacf10

short.name =

pacf10

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'rw1'.

Properties:

doc =

'Random walk of order 1'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

43001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2'.

Properties:

doc =

'Random walk of order 2'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

44001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'besag'.

Properties:

doc =

'Besag model'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

45001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'iid'.

Properties:

doc =

'Independent model'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

46001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'scopy'

Valid models in this section are:

Model 'rw1'.

Properties:

doc =

'Random walk of order 1'

Number of hyperparmeters is 0.

Model 'rw2'.

Properties:

doc =

'Random walk of order 2'

Number of hyperparmeters is 0.

'mix'

Valid models in this section are:

Model 'gaussian'.

Properties:

doc =

'Gaussian mixture'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

47001

name =

log precision

short.name =

prec

prior =

pc.prec

param =

1 0.01

initial =

0

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'loggamma'.

Properties:

doc =

'LogGamma mixture'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

47101

name =

log precision

short.name =

prec

prior =

pc.mgamma

param =

4.8

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'mloggamma'.

Properties:

doc =

'Minus-LogGamma mixture'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

47201

name =

log precision

short.name =

prec

prior =

pc.mgamma

param =

4.8

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'link'

Valid models in this section are:

Model 'default'.

Properties:

doc =

'The default link'

Number of hyperparmeters is 0.

Model 'cloglog'.

Properties:

doc =

'The complementary log-log link'

Number of hyperparmeters is 0.

Model 'ccloglog'.

Properties:

doc =

'The complement complementary log-log link'

Number of hyperparmeters is 0.

Model 'loglog'.

Properties:

doc =

'The log-log link'

Number of hyperparmeters is 0.

Model 'identity'.

Properties:

doc =

'The identity link'

Number of hyperparmeters is 0.

Model 'inverse'.

Properties:

doc =

'The inverse link'

Number of hyperparmeters is 0.

Model 'log'.

Properties:

doc =

'The log-link'

Number of hyperparmeters is 0.

Model 'loga'.

Properties:

doc =

'The loga-link'

Number of hyperparmeters is 0.

Model 'neglog'.

Properties:

doc =

'The negative log-link'

Number of hyperparmeters is 0.

Model 'logit'.

Properties:

doc =

'The logit-link'

Number of hyperparmeters is 0.

Model 'probit'.

Properties:

doc =

'The probit-link'

Number of hyperparmeters is 0.

Model 'cauchit'.

Properties:

doc =

'The cauchit-link'

Number of hyperparmeters is 0.

Model 'tan'.

Properties:

doc =

'The tan-link'

Number of hyperparmeters is 0.

Model 'quantile'.

Properties:

doc =

'The quantile-link'

Number of hyperparmeters is 0.

Model 'pquantile'.

Properties:

doc =

'The population quantile-link'

Number of hyperparmeters is 0.

Model 'sslogit'.

Properties:

doc =

'Logit link with sensitivity and specificity'

status =

'disabled'

pdf =

'NA'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

48001

name =

sensitivity

short.name =

sens

prior =

logitbeta

param =

10 5

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta2'

hyperid =

48002

name =

specificity

short.name =

spec

prior =

logitbeta

param =

10 5

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'logoffset'.

Properties:

doc =

'Log-link with an offset'

pdf =

'logoffset'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

49001

name =

beta

short.name =

b

prior =

normal

param =

0 100

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'logitoffset'.

Properties:

doc =

'Logit-link with an offset'

status =

'experimental'

pdf =

'logitoffset'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

49011

name =

prob

short.name =

p

prior =

normal

param =

-1 100

initial =

-1

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'robit'.

Properties:

doc =

'Robit link'

status =

'experimental'

pdf =

'robit'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

49021

name =

log degrees of freedom

short.name =

dof

initial =

1.6094379124341

fixed =

TRUE

prior =

pc.dof

param =

50 0.5

to.theta =

function(x) log(x - 2)

from.theta =

function(x) 2 + exp(x)

Model 'sn'.

Properties:

doc =

'Skew-normal link'

pdf =

'linksn'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

49031

name =

skewness

short.name =

skew

initial =

0.00123456789

fixed =

FALSE

prior =

pc.sn

param =

10

to.theta =

function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))

from.theta =

function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)

Hyperparameter 'theta2'

hyperid =

49032

name =

intercept

short.name =

intercept

initial =

0

fixed =

FALSE

prior =

linksnintercept

param =

0 0

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'powerlogit'.

Properties:

doc =

'Power logit link'

pdf =

'powerlogit'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

49131

name =

power

short.name =

power

initial =

0.00123456789

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

49132

name =

intercept

short.name =

intercept

initial =

0

fixed =

FALSE

prior =

logitbeta

param =

1 1

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'test1'.

Properties:

doc =

'A test1-link function (experimental)'

pdf =

'NA'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

50001

name =

beta

short.name =

b

prior =

normal

param =

0 100

initial =

0

fixed =

FALSE

to.theta =

function(x) x

from.theta =

function(x) x

Model 'special1'.

Properties:

doc =

'A special1-link function (experimental)'

pdf =

'NA'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

51001

name =

log precision

short.name =

prec

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

51002

name =

beta1

short.name =

beta1

initial =

0

fixed =

FALSE

prior =

mvnorm

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

51003

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

51004

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

51005

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

51006

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

51007

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

51008

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

51009

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

51010

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

51011

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model 'special2'.

Properties:

doc =

'A special2-link function (experimental)'

pdf =

'NA'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

52001

name =

beta

short.name =

b

prior =

normal

param =

0 10

initial =

0

fixed =

FALSE

to.theta =

function(x) x

from.theta =

function(x) x

'predictor'

Valid models in this section are:

Model 'predictor'.

Properties:

doc =

'(do not use)'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

53001

name =

log precision

short.name =

prec

initial =

13.8155105579643

fixed =

TRUE

prior =

loggamma

param =

1 1e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'hazard'

Valid models in this section are:

Model 'rw1'.

Properties:

doc =

'A random walk of order 1 for the log-hazard'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

54001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2'.

Properties:

doc =

'A random walk of order 2 for the log-hazard'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

55001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'iid'.

Properties:

doc =

'An iid model for the log-hazard'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

55501

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'likelihood'

Valid models in this section are:

Model 'poisson'.

Properties:

doc =

'The Poisson likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log logoffset quantile test1 special1 special2'

pdf =

'poisson'

Number of hyperparmeters is 0.

Model 'xpoisson'.

Properties:

doc =

'The Poisson likelihood (expert version)'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log logoffset quantile test1 special1 special2'

pdf =

'poisson'

Number of hyperparmeters is 0.

Model 'cenpoisson'.

Properties:

doc =

'Then censored Poisson likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log logoffset test1 special1 special2'

pdf =

'cenpoisson'

Number of hyperparmeters is 0.

Model 'cenpoisson2'.

Properties:

doc =

'Then censored Poisson likelihood (version 2)'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log logoffset test1 special1 special2'

pdf =

'cenpoisson2'

Number of hyperparmeters is 0.

Model 'gpoisson'.

Properties:

doc =

'The generalized Poisson likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log logoffset'

pdf =

'gpoisson'

status =

'experimental'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

56001

name =

overdispersion

short.name =

phi

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

56002

name =

p

short.name =

p

initial =

1

fixed =

TRUE

prior =

normal

param =

1 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'poisson.special1'.

Properties:

doc =

'The Poisson.special1 likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log'

pdf =

'poisson-special'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

56100

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model '0poisson'.

Properties:

doc =

'New 0-inflated Poisson'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log quantile'

link.simple =

'default logit cauchit probit cloglog ccloglog'

pdf =

'0inflated'

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56201

name =

beta1

short.name =

beta1

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56202

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56203

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56204

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56205

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56206

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56207

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56208

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56209

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56210

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model '0poissonS'.

Properties:

doc =

'New 0-inflated Poisson Swap'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit'

link.simple =

'default log'

pdf =

'0inflated'

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56301

name =

beta1

short.name =

beta1

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56302

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56303

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56304

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56305

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56306

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56307

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56308

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56309

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56310

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model 'bell'.

Properties:

doc =

'The Bell likelihood'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log'

pdf =

'bell'

Number of hyperparmeters is 0.

Model '0binomial'.

Properties:

doc =

'New 0-inflated Binomial'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog log'

link.simple =

'default logit cauchit probit cloglog ccloglog'

pdf =

'0inflated'

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56401

name =

beta1

short.name =

beta1

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56402

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56403

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56404

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56405

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56406

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56407

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56408

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56409

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56410

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model '0binomialS'.

Properties:

doc =

'New 0-inflated Binomial Swap'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog log'

link.simple =

'default logit cauchit probit cloglog ccloglog'

pdf =

'0inflated'

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56501

name =

beta1

short.name =

beta1

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56502

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56503

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56504

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56505

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56506

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56507

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56508

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56509

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56510

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model 'binomial'.

Properties:

doc =

'The Binomial likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit'

pdf =

'binomial'

Number of hyperparmeters is 0.

Model 'xbinomial'.

Properties:

doc =

'The Binomial likelihood (expert version)'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit'

pdf =

'binomial'

status =

'experimental'

Number of hyperparmeters is 0.

Model 'pom'.

Properties:

doc =

'Likelihood for the proportional odds model'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default identity'

pdf =

'pom'

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

57101

name =

theta1

short.name =

theta1

initial =

NA

fixed =

FALSE

prior =

dirichlet

param =

3

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

57102

name =

theta2

short.name =

theta2

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

57103

name =

theta3

short.name =

theta3

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

57104

name =

theta4

short.name =

theta4

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

57105

name =

theta5

short.name =

theta5

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

57106

name =

theta6

short.name =

theta6

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta7'

hyperid =

57107

name =

theta7

short.name =

theta7

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta8'

hyperid =

57108

name =

theta8

short.name =

theta8

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta9'

hyperid =

57109

name =

theta9

short.name =

theta9

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta10'

hyperid =

57110

name =

theta10

short.name =

theta10

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'bgev'.

Properties:

doc =

'The blended Generalized Extreme Value likelihood'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity log'

pdf =

'bgev'

Number of hyperparmeters is 12.

Hyperparameter 'theta1'

hyperid =

57201

name =

spread

short.name =

sd

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 3

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

57202

name =

tail

short.name =

xi

initial =

-4

fixed =

FALSE

prior =

pc.gevtail

param =

7 0 0.5

to.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Hyperparameter 'theta3'

hyperid =

57203

name =

beta1

short.name =

beta1

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

57204

name =

beta2

short.name =

beta2

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

57205

name =

beta3

short.name =

beta3

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

57206

name =

beta4

short.name =

beta4

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

57207

name =

beta5

short.name =

beta5

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

57208

name =

beta6

short.name =

beta6

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

57209

name =

beta7

short.name =

beta7

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

57210

name =

beta8

short.name =

beta8

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

57211

name =

beta9

short.name =

beta9

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

57212

name =

beta10

short.name =

beta

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Model 'gamma'.

Properties:

doc =

'The Gamma likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log quantile'

pdf =

'gamma'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

58001

name =

precision parameter

short.name =

prec

initial =

4.60517018598809

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'gammasurv'.

Properties:

doc =

'The Gamma likelihood (survival)'

survival =

'TRUE'

discrete =

'FALSE'

status =

'experimental'

link =

'default log neglog quantile'

pdf =

'gammasurv'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

58101

name =

precision parameter

short.name =

prec

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

58102

name =

beta1

short.name =

beta1

initial =

-7

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

58103

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

58104

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

58105

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

58106

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

58107

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

58108

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

58109

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

58110

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

58111

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'gammajw'.

Properties:

doc =

'A special case of the Gamma likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log neglog'

pdf =

'gammajw'

Number of hyperparmeters is 0.

Model 'gammajwsurv'.

Properties:

doc =

'A special case of the Gamma likelihood (survival)'

survival =

'TRUE'

discrete =

'FALSE'

link =

'default log'

pdf =

'gammajw'

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

58200

name =

beta1

short.name =

beta1

initial =

-7

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

58201

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

58202

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

58203

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

58204

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

58205

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

58206

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

58207

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

58208

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

58209

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'gammacount'.

Properties:

doc =

'A Gamma generalisation of the Poisson likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

status =

'experimental'

pdf =

'gammacount'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

59001

name =

log alpha

short.name =

alpha

initial =

0

fixed =

FALSE

prior =

pc.gammacount

param =

3

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'qkumar'.

Properties:

doc =

'A quantile version of the Kumar likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga cauchit'

pdf =

'qkumar'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

60001

name =

precision parameter

short.name =

prec

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 0.1

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Model 'qloglogistic'.

Properties:

doc =

'A quantile loglogistic likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log neglog'

pdf =

'qloglogistic'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

60011

name =

log alpha

short.name =

alpha

initial =

1

fixed =

FALSE

prior =

loggamma

param =

25 25

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'qloglogisticsurv'.

Properties:

doc =

'A quantile loglogistic likelihood (survival)'

survival =

'TRUE'

discrete =

'FALSE'

link =

'default log neglog'

pdf =

'qloglogistic'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

60021

name =

log alpha

short.name =

alpha

initial =

1

fixed =

FALSE

prior =

loggamma

param =

25 25

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

60022

name =

beta1

short.name =

beta1

initial =

-5

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

60023

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

60024

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

60025

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

60026

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

60027

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

60028

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

60029

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

60030

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

60031

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'beta'.

Properties:

doc =

'The Beta likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog'

pdf =

'beta'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

61001

name =

precision parameter

short.name =

phi

initial =

2.30258509299405

fixed =

FALSE

prior =

loggamma

param =

1 0.1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'betabinomial'.

Properties:

doc =

'The Beta-Binomial likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'betabinomial'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

62001

name =

overdispersion

short.name =

rho

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 0.4

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'betabinomialna'.

Properties:

doc =

'The Beta-Binomial Normal approximation likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'betabinomialna'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

62101

name =

overdispersion

short.name =

rho

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 0.4

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'cbinomial'.

Properties:

doc =

'The clustered Binomial likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

status =

'experimental'

pdf =

'cbinomial'

Number of hyperparmeters is 0.

Model 'nbinomial'.

Properties:

doc =

'The negBinomial likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log logoffset quantile'

pdf =

'nbinomial'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

63001

name =

size

short.name =

size

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'nbinomial2'.

Properties:

doc =

'The negBinomial2 likelihood'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog'

pdf =

'nbinomial'

Number of hyperparmeters is 0.

Model 'cennbinomial2'.

Properties:

doc =

'The CenNegBinomial2 likelihood (similar to cenpoisson2)'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default log logoffset quantile'

pdf =

'cennbinomial2'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

63101

name =

size

short.name =

size

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'simplex'.

Properties:

doc =

'The simplex likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog'

pdf =

'simplex'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

64001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'gaussian'.

Properties:

doc =

'The Gaussian likelihoood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity logit loga cauchit log logoffset'

pdf =

'gaussian'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

65001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

65002

name =

log precision offset

short.name =

precoffset

initial =

72.0873067782343

fixed =

TRUE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'gaussianjw'.

Properties:

doc =

'The GaussianJW likelihoood'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit probit'

pdf =

'gaussianjw'

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

65101

name =

beta1

short.name =

beta1

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

65102

name =

beta2

short.name =

beta2

initial =

1

fixed =

FALSE

prior =

normal

param =

1 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

65103

name =

beta3

short.name =

beta3

initial =

-1

fixed =

FALSE

prior =

normal

param =

-1 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'agaussian'.

Properties:

doc =

'The aggregated Gaussian likelihoood'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity logit loga cauchit log logoffset'

pdf =

'agaussian'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

66001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'circularnormal'.

Properties:

doc =

'The circular Gaussian likelihoood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default tan'

pdf =

'circular-normal'

status =

'experimental'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

67001

name =

log precision parameter

short.name =

prec

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'wrappedcauchy'.

Properties:

doc =

'The wrapped Cauchy likelihoood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default tan'

pdf =

'wrapped-cauchy'

status =

'disabled'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

68001

name =

log precision parameter

short.name =

prec

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 0.005

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'iidgamma'.

Properties:

doc =

'(experimental)'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity'

pdf =

'iidgamma'

status =

'experimental'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

69001

name =

logshape

short.name =

shape

initial =

0

fixed =

FALSE

prior =

loggamma

param =

100 100

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

69002

name =

lograte

short.name =

rate

initial =

0

fixed =

FALSE

prior =

loggamma

param =

100 100

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'iidlogitbeta'.

Properties:

doc =

'(experimental)'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga'

pdf =

'iidlogitbeta'

status =

'experimental'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

70001

name =

log.a

short.name =

a

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

70002

name =

log.b

short.name =

b

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'loggammafrailty'.

Properties:

doc =

'(experimental)'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity'

pdf =

'loggammafrailty'

status =

'experimental'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

71001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'logistic'.

Properties:

doc =

'The Logistic likelihoood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity'

pdf =

'logistic'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

72001

name =

log precision

short.name =

prec

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'sn'.

Properties:

doc =

'The Skew-Normal likelihoood'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity'

pdf =

'sn'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

74001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

74002

name =

logit skew

short.name =

skew

initial =

0.00123456789

fixed =

FALSE

prior =

pc.sn

param =

10

to.theta =

function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))

from.theta =

function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)

Model 'gev'.

Properties:

doc =

'The Generalized Extreme Value likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity'

status =

'disabled: Use likelihood model 'bgev' instead; see inla.doc('bgev')'

pdf =

'gev'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

76001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

76002

name =

tail parameter

short.name =

tail

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 25

to.theta =

function(x) x

from.theta =

function(x) x

Model 'lognormal'.

Properties:

doc =

'The log-Normal likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity'

pdf =

'lognormal'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

77101

name =

log precision

short.name =

prec

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'lognormalsurv'.

Properties:

doc =

'The log-Normal likelihood (survival)'

survival =

'TRUE'

discrete =

'FALSE'

link =

'default identity'

pdf =

'lognormal'

Number of hyperparmeters is 11.

Hyperparameter 'theta'

hyperid =

78001

name =

log precision

short.name =

prec

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

78002

name =

beta1

short.name =

beta1

initial =

-7

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

78003

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

78004

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

78005

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

78006

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

78007

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

78008

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

78009

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

78010

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

78011

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'exponential'.

Properties:

doc =

'The Exponential likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'exponential'

Number of hyperparmeters is 0.

Model 'exponentialsurv'.

Properties:

doc =

'The Exponential likelihood (survival)'

survival =

'TRUE'

discrete =

'FALSE'

link =

'default log neglog'

pdf =

'exponential'

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

78020

name =

beta1

short.name =

beta1

initial =

-4

fixed =

FALSE

prior =

normal

param =

-1 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

78021

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

78022

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

78023

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

78024

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

78025

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

78026

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

78027

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

78028

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

78029

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'coxph'.

Properties:

doc =

'Cox-proportional hazard likelihood'

survival =

'TRUE'

discrete =

'TRUE'

link =

'default log neglog'

pdf =

'coxph'

Number of hyperparmeters is 0.

Model 'weibull'.

Properties:

doc =

'The Weibull likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log neglog quantile'

pdf =

'weibull'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

79001

name =

log alpha

short.name =

alpha

initial =

-2

fixed =

FALSE

prior =

pc.alphaw

param =

5

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Model 'weibullsurv'.

Properties:

doc =

'The Weibull likelihood (survival)'

survival =

'TRUE'

discrete =

'FALSE'

link =

'default log neglog quantile'

pdf =

'weibull'

Number of hyperparmeters is 11.

Hyperparameter 'theta'

hyperid =

79101

name =

log alpha

short.name =

alpha

initial =

-2

fixed =

FALSE

prior =

pc.alphaw

param =

5

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Hyperparameter 'theta2'

hyperid =

79102

name =

beta1

short.name =

beta1

initial =

-7

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

79103

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

79104

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

79105

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

79106

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

79107

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

79108

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

79109

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

79110

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

79111

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'loglogistic'.

Properties:

doc =

'The loglogistic likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log neglog'

pdf =

'loglogistic'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

80001

name =

log alpha

short.name =

alpha

initial =

1

fixed =

FALSE

prior =

loggamma

param =

25 25

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'loglogisticsurv'.

Properties:

doc =

'The loglogistic likelihood (survival)'

survival =

'TRUE'

discrete =

'FALSE'

link =

'default log neglog'

pdf =

'loglogistic'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

80011

name =

log alpha

short.name =

alpha

initial =

1

fixed =

FALSE

prior =

loggamma

param =

25 25

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

80012

name =

beta1

short.name =

beta1

initial =

-5

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

80013

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

80014

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

80015

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

80016

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

80017

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

80018

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

80019

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

80020

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

80021

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'stochvol'.

Properties:

doc =

'The Gaussian stochvol likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'stochvolgaussian'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

82001

name =

log precision

short.name =

prec

initial =

500

fixed =

TRUE

prior =

loggamma

param =

1 0.005

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'stochvolsn'.

Properties:

doc =

'The SkewNormal stochvol likelihood'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'stochvolsn'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

82101

name =

logit skew

short.name =

skew

initial =

0.00123456789

fixed =

FALSE

prior =

pc.sn

param =

10

to.theta =

function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))

from.theta =

function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)

Hyperparameter 'theta2'

hyperid =

82102

name =

log precision

short.name =

prec

initial =

500

fixed =

TRUE

prior =

loggamma

param =

1 0.005

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'stochvolt'.

Properties:

doc =

'The Student-t stochvol likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'stochvolt'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

83001

name =

log degrees of freedom

short.name =

dof

initial =

4

fixed =

FALSE

prior =

pc.dof

param =

15 0.5

to.theta =

function(x) log(x - 2)

from.theta =

function(x) 2 + exp(x)

Model 'stochvolnig'.

Properties:

doc =

'The Normal inverse Gaussian stochvol likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'stochvolnig'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

84001

name =

skewness

short.name =

skew

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

84002

name =

shape

short.name =

shape

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 0.5

to.theta =

function(x) log(x - 1)

from.theta =

function(x) 1 + exp(x)

Model 'zeroinflatedpoisson0'.

Properties:

doc =

'Zero-inflated Poisson, type 0'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

85001

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedpoisson1'.

Properties:

doc =

'Zero-inflated Poisson, type 1'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

86001

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedpoisson2'.

Properties:

doc =

'Zero-inflated Poisson, type 2'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

87001

name =

log alpha

short.name =

a

initial =

0.693147180559945

fixed =

FALSE

prior =

gaussian

param =

0.693147180559945 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroinflatedcenpoisson0'.

Properties:

doc =

'Zero-inflated censored Poisson, type 0'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

87101

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedcenpoisson1'.

Properties:

doc =

'Zero-inflated censored Poisson, type 1'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

87201

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbetabinomial0'.

Properties:

doc =

'Zero-inflated Beta-Binomial, type 0'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'zeroinflated'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

88001

name =

overdispersion

short.name =

rho

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 0.4

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta2'

hyperid =

88002

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbetabinomial1'.

Properties:

doc =

'Zero-inflated Beta-Binomial, type 1'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'zeroinflated'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

89001

name =

overdispersion

short.name =

rho

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 0.4

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta2'

hyperid =

89002

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbinomial0'.

Properties:

doc =

'Zero-inflated Binomial, type 0'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'zeroinflated'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

90001

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbinomial1'.

Properties:

doc =

'Zero-inflated Binomial, type 1'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'zeroinflated'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

91001

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbinomial2'.

Properties:

doc =

'Zero-inflated Binomial, type 2'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'zeroinflated'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

92001

name =

alpha

short.name =

alpha

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroninflatedbinomial2'.

Properties:

doc =

'Zero and N inflated binomial, type 2'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'NA'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

93001

name =

alpha1

short.name =

alpha1

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

93002

name =

alpha2

short.name =

alpha2

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroninflatedbinomial3'.

Properties:

doc =

'Zero and N inflated binomial, type 3'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'zeroinflated'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

93101

name =

alpha0

short.name =

alpha0

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

93102

name =

alphaN

short.name =

alphaN

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroinflatedbetabinomial2'.

Properties:

doc =

'Zero inflated Beta-Binomial, type 2'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default logit loga cauchit probit cloglog ccloglog loglog robit sn'

pdf =

'zeroinflated'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

94001

name =

log alpha

short.name =

a

initial =

0.693147180559945

fixed =

FALSE

prior =

gaussian

param =

0.693147180559945 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

94002

name =

beta

short.name =

b

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroinflatednbinomial0'.

Properties:

doc =

'Zero inflated negBinomial, type 0'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

95001

name =

log size

short.name =

size

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

95002

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatednbinomial1'.

Properties:

doc =

'Zero inflated negBinomial, type 1'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

96001

name =

log size

short.name =

size

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

96002

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatednbinomial1strata2'.

Properties:

doc =

'Zero inflated negBinomial, type 1, strata 2'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

97001

name =

log size

short.name =

size

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

97002

name =

logit probability 1

short.name =

prob1

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta3'

hyperid =

97003

name =

logit probability 2

short.name =

prob2

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta4'

hyperid =

97004

name =

logit probability 3

short.name =

prob3

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta5'

hyperid =

97005

name =

logit probability 4

short.name =

prob4

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta6'

hyperid =

97006

name =

logit probability 5

short.name =

prob5

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta7'

hyperid =

97007

name =

logit probability 6

short.name =

prob6

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta8'

hyperid =

97008

name =

logit probability 7

short.name =

prob7

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta9'

hyperid =

97009

name =

logit probability 8

short.name =

prob8

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta10'

hyperid =

97010

name =

logit probability 9

short.name =

prob9

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta11'

hyperid =

97011

name =

logit probability 10

short.name =

prob10

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatednbinomial1strata3'.

Properties:

doc =

'Zero inflated negBinomial, type 1, strata 3'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

98001

name =

logit probability

short.name =

prob

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta2'

hyperid =

98002

name =

log size 1

short.name =

size1

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

98003

name =

log size 2

short.name =

size2

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

98004

name =

log size 3

short.name =

size3

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

98005

name =

log size 4

short.name =

size4

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

98006

name =

log size 5

short.name =

size5

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta7'

hyperid =

98007

name =

log size 6

short.name =

size6

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta8'

hyperid =

98008

name =

log size 7

short.name =

size7

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta9'

hyperid =

98009

name =

log size 8

short.name =

size8

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta10'

hyperid =

98010

name =

log size 9

short.name =

size9

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta11'

hyperid =

98011

name =

log size 10

short.name =

size10

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroinflatednbinomial2'.

Properties:

doc =

'Zero inflated negBinomial, type 2'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'zeroinflated'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

99001

name =

log size

short.name =

size

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

99002

name =

log alpha

short.name =

a

initial =

0.693147180559945

fixed =

FALSE

prior =

gaussian

param =

2 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 't'.

Properties:

doc =

'Student-t likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity'

pdf =

'student-t'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

100001

name =

log precision

short.name =

prec

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

100002

name =

log degrees of freedom

short.name =

dof

initial =

5

fixed =

FALSE

prior =

pc.dof

param =

15 0.5

to.theta =

function(x) log(x - 2)

from.theta =

function(x) 2 + exp(x)

Model 'tstrata'.

Properties:

doc =

'A stratified version of the Student-t likelihood'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity'

pdf =

'tstrata'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

101001

name =

log degrees of freedom

short.name =

dof

initial =

4

fixed =

FALSE

prior =

pc.dof

param =

15 0.5

to.theta =

function(x) log(x - 5)

from.theta =

function(x) 5 + exp(x)

Hyperparameter 'theta2'

hyperid =

101002

name =

log precision1

short.name =

prec1

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

101003

name =

log precision2

short.name =

prec2

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

101004

name =

log precision3

short.name =

prec3

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

101005

name =

log precision4

short.name =

prec4

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

101006

name =

log precision5

short.name =

prec5

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta7'

hyperid =

101007

name =

log precision6

short.name =

prec6

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta8'

hyperid =

101008

name =

log precision7

short.name =

prec7

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta9'

hyperid =

101009

name =

log precision8

short.name =

prec8

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta10'

hyperid =

101010

name =

log precision9

short.name =

prec9

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta11'

hyperid =

101011

name =

log precision10

short.name =

prec10

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'nmix'.

Properties:

doc =

'Binomial-Poisson mixture'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga probit'

pdf =

'nmix'

Number of hyperparmeters is 15.

Hyperparameter 'theta1'

hyperid =

101101

name =

beta1

short.name =

beta1

initial =

2.30258509299405

fixed =

FALSE

prior =

normal

param =

0 0.5

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

101102

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

101103

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

101104

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

101105

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

101106

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

101107

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

101108

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

101109

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

101110

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

101111

name =

beta11

short.name =

beta11

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

101112

name =

beta12

short.name =

beta12

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

101113

name =

beta13

short.name =

beta13

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

101114

name =

beta14

short.name =

beta14

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

101115

name =

beta15

short.name =

beta15

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Model 'nmixnb'.

Properties:

doc =

'NegBinomial-Poisson mixture'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default logit loga probit'

pdf =

'nmixnb'

Number of hyperparmeters is 16.

Hyperparameter 'theta1'

hyperid =

101121

name =

beta1

short.name =

beta1

initial =

2.30258509299405

fixed =

FALSE

prior =

normal

param =

0 0.5

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

101122

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

101123

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

101124

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

101125

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

101126

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

101127

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

101128

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

101129

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

101130

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

101131

name =

beta11

short.name =

beta11

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

101132

name =

beta12

short.name =

beta12

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

101133

name =

beta13

short.name =

beta13

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

101134

name =

beta14

short.name =

beta14

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

101135

name =

beta15

short.name =

beta15

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

101136

name =

overdispersion

short.name =

overdispersion

initial =

0

fixed =

FALSE

prior =

pc.gamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'gp'.

Properties:

doc =

'Generalized Pareto likelihood'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default quantile'

pdf =

'genPareto'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

101201

name =

tail

short.name =

xi

initial =

-4

fixed =

FALSE

prior =

pc.gevtail

param =

7 0 0.5

to.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Model 'dgp'.

Properties:

doc =

'Discrete generalized Pareto likelihood'

status =

'experimental'

survival =

'FALSE'

discrete =

'TRUE'

link =

'default quantile'

pdf =

'dgp'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

101301

name =

tail

short.name =

xi

initial =

2

fixed =

FALSE

prior =

pc.gevtail

param =

7 0 0.5

to.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Model 'logperiodogram'.

Properties:

doc =

'Likelihood for the log-periodogram'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default identity'

pdf =

'NA'

Number of hyperparmeters is 0.

Model 'tweedie'.

Properties:

doc =

'Tweedie distribution'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'tweedie'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

102101

name =

p

short.name =

p

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x, interval = c(1.0, 2.0)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(1.0, 2.0)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Hyperparameter 'theta2'

hyperid =

102201

name =

dispersion

short.name =

phi

initial =

-4

fixed =

FALSE

prior =

loggamma

param =

100 100

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'fmri'.

Properties:

doc =

'fmri distribution (special nc-chi)'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log'

pdf =

'fmri'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

103101

name =

precision

short.name =

prec

initial =

0

fixed =

FALSE

prior =

loggamma

param =

10 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

103202

name =

dof

short.name =

df

initial =

4

fixed =

TRUE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Model 'fmrisurv'.

Properties:

doc =

'fmri distribution (special nc-chi)'

status =

'experimental'

survival =

'TRUE'

discrete =

'FALSE'

link =

'default log'

pdf =

'fmri'

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

104101

name =

precision

short.name =

prec

initial =

0

fixed =

FALSE

prior =

loggamma

param =

10 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

104201

name =

dof

short.name =

df

initial =

4

fixed =

TRUE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Model 'gompertz'.

Properties:

doc =

'gompertz distribution'

status =

'experimental'

survival =

'FALSE'

discrete =

'FALSE'

link =

'default log neglog'

pdf =

'gompertz'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

105101

name =

shape

short.name =

alpha

initial =

-1

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Model 'gompertzsurv'.

Properties:

doc =

'gompertz distribution'

status =

'experimental'

survival =

'TRUE'

discrete =

'FALSE'

link =

'default log neglog'

pdf =

'gompertz'

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

106101

name =

shape

short.name =

alpha

initial =

-10

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Hyperparameter 'theta2'

hyperid =

106102

name =

beta1

short.name =

beta1

initial =

-5

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

106103

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

106104

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

106105

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

106106

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

106107

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

106108

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

106109

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

106110

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

106111

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

'prior'

Valid models in this section are:

Model 'normal'.

Number of parameters in the prior = 2

Model 'gaussian'.

Number of parameters in the prior = 2

Model 'linksnintercept'.

Number of parameters in the prior = 2

Model 'wishart1d'.

Number of parameters in the prior = 2

Model 'wishart2d'.

Number of parameters in the prior = 4

Model 'wishart3d'.

Number of parameters in the prior = 7

Model 'wishart4d'.

Number of parameters in the prior = 11

Model 'wishart5d'.

Number of parameters in the prior = 16

Model 'loggamma'.

Number of parameters in the prior = 2

Model 'gamma'.

Number of parameters in the prior = 2

Model 'minuslogsqrtruncnormal'.

Number of parameters in the prior = 2

Model 'logtnormal'.

Number of parameters in the prior = 2

Model 'logtgaussian'.

Number of parameters in the prior = 2

Model 'flat'.

Number of parameters in the prior = 0

Model 'logflat'.

Number of parameters in the prior = 0

Model 'logiflat'.

Number of parameters in the prior = 0

Model 'mvnorm'.

Number of parameters in the prior = -1

Model 'pc.alphaw'.

Number of parameters in the prior = 1

Model 'pc.ar'.

Number of parameters in the prior = 1

Model 'dirichlet'.

Number of parameters in the prior = 1

Model 'none'.

Number of parameters in the prior = 0

Model 'invalid'.

Number of parameters in the prior = 0

Model 'betacorrelation'.

Number of parameters in the prior = 2

Model 'logitbeta'.

Number of parameters in the prior = 2

Model 'pc.prec'.

Number of parameters in the prior = 2

Model 'pc.dof'.

Number of parameters in the prior = 2

Model 'pc.cor0'.

Number of parameters in the prior = 2

Model 'pc.cor1'.

Number of parameters in the prior = 2

Model 'pc.fgnh'.

Number of parameters in the prior = 2

Model 'pc.spde.GA'.

Number of parameters in the prior = 4

Model 'pc.matern'.

Number of parameters in the prior = 3

Model 'pc.range'.

Number of parameters in the prior = 2

Model 'pc.sn'.

Number of parameters in the prior = 1

Model 'pc.gamma'.

Number of parameters in the prior = 1

Model 'pc.mgamma'.

Number of parameters in the prior = 1

Model 'pc.gammacount'.

Number of parameters in the prior = 1

Model 'pc.gevtail'.

Number of parameters in the prior = 3

Model 'pc'.

Number of parameters in the prior = 2

Model 'ref.ar'.

Number of parameters in the prior = 0

Model 'pom'.

Number of parameters in the prior = 0

Model 'jeffreystdf'.

Number of parameters in the prior = 0

Model 'wishartkd'.

Number of parameters in the prior = 211

Model 'expression:'.

Number of parameters in the prior = -1

Model 'table:'.

Number of parameters in the prior = -1

'wrapper'

Valid models in this section are:

Model 'joint'.

Properties:

doc =

'(experimental)'

constr =

'FALSE'

nrow.ncol =

'FALSE'

augmented =

'FALSE'

aug.factor =

'1'

aug.constr =

'NULL'

n.div.by =

'NULL'

n.required =

'FALSE'

set.default.values =

'FALSE'

pdf =

'NA'

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

102001

name =

log precision

short.name =

prec

initial =

0

fixed =

TRUE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'lp.scale'

Valid models in this section are:

Model 'lp.scale'.

Properties:

pdf =

'lp.scale'

Number of hyperparmeters is 100.

Hyperparameter 'theta1'

hyperid =

103001

name =

beta1

short.name =

b1

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

103002

name =

beta2

short.name =

b2

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

103003

name =

beta3

short.name =

b3

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

103004

name =

beta4

short.name =

b4

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

103005

name =

beta5

short.name =

b5

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

103006

name =

beta6

short.name =

b6

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

103007

name =

beta7

short.name =

b7

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

103008

name =

beta8

short.name =

b8

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

103009

name =

beta9

short.name =

b9

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

103010

name =

beta10

short.name =

b10

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

103011

name =

beta11

short.name =

b11

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

103012

name =

beta12

short.name =

b12

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

103013

name =

beta13

short.name =

b13

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

103014

name =

beta14

short.name =

b14

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

103015

name =

beta15

short.name =

b15

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

103016

name =

beta16

short.name =

b16

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

103017

name =

beta17

short.name =

b17

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

103018

name =

beta18

short.name =

b18

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

103019

name =

beta19

short.name =

b19

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

103020

name =

beta20

short.name =

b20

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

103021

name =

beta21

short.name =

b21

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

103022

name =

beta22

short.name =

b22

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

103023

name =

beta23

short.name =

b23

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

103024

name =

beta24

short.name =

b24

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

103025

name =

beta25

short.name =

b25

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

103026

name =

beta26

short.name =

b26

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

103027

name =

beta27

short.name =

b27

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

103028

name =

beta28

short.name =

b28

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

103029

name =

beta29

short.name =

b29

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

103030

name =

beta30

short.name =

b30

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

103031

name =

beta31

short.name =

b31

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

103032

name =

beta32

short.name =

b32

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

103033

name =

beta33

short.name =

b33

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

103034

name =

beta34

short.name =

b34

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

103035

name =

beta35

short.name =

b35

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

103036

name =

beta36

short.name =

b36

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

103037

name =

beta37

short.name =

b37

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

103038

name =

beta38

short.name =

b38

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

103039

name =

beta39

short.name =

b39

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

103040

name =

beta40

short.name =

b40

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

103041

name =

beta41

short.name =

b41

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

103042

name =

beta42

short.name =

b42

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

103043

name =

beta43

short.name =

b43

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

103044

name =

beta44

short.name =

b44

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

103045

name =

beta45

short.name =

b45

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

103046

name =

beta46

short.name =

b46

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

103047

name =

beta47

short.name =

b47

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

103048

name =

beta48

short.name =

b48

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

103049

name =

beta49

short.name =

b49

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

103050

name =

beta50

short.name =

b50

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

103051

name =

beta51

short.name =

b51

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta52'

hyperid =

103052

name =

beta52

short.name =

b52

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta53'

hyperid =

103053

name =

beta53

short.name =

b53

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta54'

hyperid =

103054

name =

beta54

short.name =

b54

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta55'

hyperid =

103055

name =

beta55

short.name =

b55

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta56'

hyperid =

103056

name =

beta56

short.name =

b56

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta57'

hyperid =

103057

name =

beta57

short.name =

b57

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta58'

hyperid =

103058

name =

beta58

short.name =

b58

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta59'

hyperid =

103059

name =

beta59

short.name =

b59

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta60'

hyperid =

103060

name =

beta60

short.name =

b60

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta61'

hyperid =

103061

name =

beta61

short.name =

b61

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta62'

hyperid =

103062

name =

beta62

short.name =

b62

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta63'

hyperid =

103063

name =

beta63

short.name =

b63

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta64'

hyperid =

103064

name =

beta64

short.name =

b64

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta65'

hyperid =

103065

name =

beta65

short.name =

b65

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta66'

hyperid =

103066

name =

beta66

short.name =

b66

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta67'

hyperid =

103067

name =

beta67

short.name =

b67

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta68'

hyperid =

103068

name =

beta68

short.name =

b68

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta69'

hyperid =

103069

name =

beta69

short.name =

b69

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta70'

hyperid =

103070

name =

beta70

short.name =

b70

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta71'

hyperid =

103071

name =

beta71

short.name =

b71

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta72'

hyperid =

103072

name =

beta72

short.name =

b72

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta73'

hyperid =

103073

name =

beta73

short.name =

b73

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta74'

hyperid =

103074

name =

beta74

short.name =

b74

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta75'

hyperid =

103075

name =

beta75

short.name =

b75

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta76'

hyperid =

103076

name =

beta76

short.name =

b76

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta77'

hyperid =

103077

name =

beta77

short.name =

b77

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta78'

hyperid =

103078

name =

beta78

short.name =

b78

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta79'

hyperid =

103079

name =

beta79

short.name =

b79

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta80'

hyperid =

103080

name =

beta80

short.name =

b80

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta81'

hyperid =

103081

name =

beta81

short.name =

b81

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta82'

hyperid =

103082

name =

beta82

short.name =

b82

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta83'

hyperid =

103083

name =

beta83

short.name =

b83

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta84'

hyperid =

103084

name =

beta84

short.name =

b84

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta85'

hyperid =

103085

name =

beta85

short.name =

b85

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta86'

hyperid =

103086

name =

beta86

short.name =

b86

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta87'

hyperid =

103087

name =

beta87

short.name =

b87

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta88'

hyperid =

103088

name =

beta88

short.name =

b88

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta89'

hyperid =

103089

name =

beta89

short.name =

b89

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta90'

hyperid =

103090

name =

beta90

short.name =

b90

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta91'

hyperid =

103091

name =

beta91

short.name =

b91

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta92'

hyperid =

103092

name =

beta92

short.name =

b92

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta93'

hyperid =

103093

name =

beta93

short.name =

b93

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta94'

hyperid =

103094

name =

beta94

short.name =

b94

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta95'

hyperid =

103095

name =

beta95

short.name =

b95

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta96'

hyperid =

103096

name =

beta96

short.name =

b96

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta97'

hyperid =

103097

name =

beta97

short.name =

b97

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta98'

hyperid =

103098

name =

beta98

short.name =

b98

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta99'

hyperid =

103099

name =

beta99

short.name =

b99

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta100'

hyperid =

103100

name =

beta100

short.name =

b100

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Examples

## How to set hyperparameters to pass as the argument 'hyper'. This
## format is compatible with the old style (using 'initial', 'fixed',
## 'prior', 'param'), but the new style using 'hyper' takes precedence
## over the old style. The two styles can also be mixed. The old style
## might be removed from the code in the future...

## Only a subset need to be given
hyper <- list(theta = list(initial = 2))
## The `name' can be used instead of 'theta', or 'theta1', 'theta2',...
hyper <- list(precision = list(initial = 2))
hyper <- list(precision = list(prior = "flat", param = numeric(0)))
hyper <- list(theta2 = list(initial = 3), theta1 = list(prior = "gaussian"))
## The 'short.name' can be used instead of 'name'
hyper <- list(rho = list(param = c(0, 1)))

---