| inla.models {INLA} | R Documentation |
This page describe the models implemented in inla, divided into sections:
latent, group, scopy, mix, link, predictor, hazard, likelihood, prior, wrapper, lp.scale.
inla.models()
Valid sections are: latent, group, scopy, mix, link, predictor, hazard, likelihood, prior, wrapper, lp.scale.
Valid models in this section are:
Alternative interface to an fixed effect
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
linear
Number of hyperparmeters is 0.
Gaussian random effects in dim=1
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
indep
Number of hyperparmeters is 1.
1001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
Classical measurement error model
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
mec
Number of hyperparmeters is 4.
2001
beta
b
gaussian
1 0.001
1
FALSE
function(x) x
function(x) x
2002
prec.u
prec
loggamma
1 1e-04
9.21034037197618
TRUE
function(x) log(x)
function(x) exp(x)
2003
mean.x
mu.x
gaussian
0 1e-04
0
TRUE
function(x) x
function(x) x
2004
prec.x
prec.x
loggamma
1 10000
-9.21034037197618
TRUE
function(x) log(x)
function(x) exp(x)
Berkson measurement error model
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
meb
Number of hyperparmeters is 2.
3001
beta
b
gaussian
1 0.001
1
FALSE
function(x) x
function(x) x
3002
prec.u
prec
loggamma
1 1e-04
6.90775527898214
FALSE
function(x) log(x)
function(x) exp(x)
Generic latent model specified using R
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
experimental
rgeneric
Number of hyperparmeters is 0.
Generic latent model specified using C
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
experimental
rgeneric
Number of hyperparmeters is 0.
Random walk of order 1
TRUE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
1e-05
rw1
Number of hyperparmeters is 1.
4001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
Random walk of order 2
TRUE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
0.001
rw2
Number of hyperparmeters is 1.
5001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
Exact solution to the random walk of order 2
TRUE
FALSE
FALSE
2
1
NULL
FALSE
FALSE
0.001
crw2
Number of hyperparmeters is 1.
6001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
Seasonal model for time series
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
seasonal
Number of hyperparmeters is 1.
7001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
The Besag area model (CAR-model)
TRUE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
besag
Number of hyperparmeters is 1.
8001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
The shared Besag model
TRUE
FALSE
FALSE
1
1 2
2
TRUE
TRUE
besag2
Number of hyperparmeters is 2.
9001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
9002
scaling parameter
a
loggamma
10 10
0
FALSE
function(x) log(x)
function(x) exp(x)
The BYM-model (Besag-York-Mollier model)
TRUE
FALSE
TRUE
2
2
NULL
TRUE
TRUE
bym
Number of hyperparmeters is 2.
10001
log unstructured precision
prec.unstruct
loggamma
1 5e-04
4
FALSE
function(x) log(x)
function(x) exp(x)
10002
log spatial precision
prec.spatial
loggamma
1 5e-04
4
FALSE
function(x) log(x)
function(x) exp(x)
The BYM-model with the PC priors
TRUE
FALSE
TRUE
2
2
NULL
TRUE
TRUE
experimental
bym2
Number of hyperparmeters is 2.
11001
log precision
prec
pc.prec
1 0.01
4
FALSE
function(x) log(x)
function(x) exp(x)
11002
logit phi
phi
pc
0.5 0.5
-3
FALSE
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
A proper version of the Besag model
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
experimental
besagproper
Number of hyperparmeters is 2.
12001
log precision
prec
loggamma
1 5e-04
2
FALSE
function(x) log(x)
function(x) exp(x)
12002
log diagonal
diag
loggamma
1 1
1
FALSE
function(x) log(x)
function(x) exp(x)
An alternative proper version of the Besag model
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
experimental
besagproper2
Number of hyperparmeters is 2.
13001
log precision
prec
loggamma
1 5e-04
2
FALSE
function(x) log(x)
function(x) exp(x)
13002
logit lambda
lambda
gaussian
0 0.45
3
FALSE
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Fractional Gaussian noise model
FALSE
FALSE
TRUE
5
1
NULL
FALSE
TRUE
4
3 4
fgn
Number of hyperparmeters is 2.
13101
log precision
prec
pc.prec
3 0.01
1
FALSE
function(x) log(x)
function(x) exp(x)
13102
logit H
H
pcfgnh
0.9 0.1
2
FALSE
function(x) log((2 * x - 1) / (2 * (1 - x)))
function(x) 0.5 + 0.5 * exp(x) / (1 + exp(x))
Fractional Gaussian noise model (alt 2)
FALSE
FALSE
TRUE
4
1
NULL
FALSE
TRUE
4
3 4
fgn
Number of hyperparmeters is 2.
13111
log precision
prec
pc.prec
3 0.01
1
FALSE
function(x) log(x)
function(x) exp(x)
13112
logit H
H
pcfgnh
0.9 0.1
2
FALSE
function(x) log((2 * x - 1) / (2 * (1 - x)))
function(x) 0.5 + 0.5 * exp(x) / (1 + exp(x))
Auto-regressive model of order 1 (AR(1))
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
ar1
Number of hyperparmeters is 3.
14001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
14002
logit lag one correlation
rho
normal
0 0.15
2
FALSE
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
14003
mean
mean
normal
0 1
0
TRUE
function(x) x
function(x) x
Auto-regressive model of order 1 w/covariates
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
TRUE
experimental
ar1c
Number of hyperparmeters is 2.
14101
log precision
prec
pc.prec
1 0.01
4
FALSE
function(x) log(x)
function(x) exp(x)
14102
logit lag one correlation
rho
pc.cor0
0.5 0.5
2
FALSE
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
Auto-regressive model of order p (AR(p))
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
ar
Number of hyperparmeters is 11.
15001
log precision
prec
4
FALSE
pc.prec
3 0.01
function(x) log(x)
function(x) exp(x)
15002
pacf1
pacf1
1
FALSE
pc.cor0
0.5 0.5
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
15003
pacf2
pacf2
0
FALSE
pc.cor0
0.5 0.4
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
15004
pacf3
pacf3
0
FALSE
pc.cor0
0.5 0.3
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
15005
pacf4
pacf4
0
FALSE
pc.cor0
0.5 0.2
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
15006
pacf5
pacf5
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
15007
pacf6
pacf6
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
15008
pacf7
pacf7
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
15009
pacf8
pacf8
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
15010
pacf9
pacf9
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
15011
pacf10
pacf10
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
The Ornstein-Uhlenbeck process
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
ou
Number of hyperparmeters is 2.
16001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
16002
log phi
phi
normal
0 0.2
-1
FALSE
function(x) log(x)
function(x) exp(x)
Intecept-slope model with Wishart-prior
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
TRUE
experimental
intslope
Number of hyperparmeters is 13.
16101
log precision1
prec1
4
FALSE
wishart2d
4 1 1 0
function(x) log(x)
function(x) exp(x)
16102
log precision2
prec2
4
FALSE
none
function(x) log(x)
function(x) exp(x)
16103
logit correlation
cor
4
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
16104
gamma1
g1
1
TRUE
normal
1 36
function(x) x
function(x) x
16105
gamma2
g2
1
TRUE
normal
1 36
function(x) x
function(x) x
16106
gamma3
g3
1
TRUE
normal
1 36
function(x) x
function(x) x
16107
gamma4
g4
1
TRUE
normal
1 36
function(x) x
function(x) x
16108
gamma5
g5
1
TRUE
normal
1 36
function(x) x
function(x) x
16109
gamma6
g6
1
TRUE
normal
1 36
function(x) x
function(x) x
16110
gamma7
g7
1
TRUE
normal
1 36
function(x) x
function(x) x
16111
gamma8
g8
1
TRUE
normal
1 36
function(x) x
function(x) x
16112
gamma9
g9
1
TRUE
normal
1 36
function(x) x
function(x) x
16113
gamma10
g10
1
TRUE
normal
1 36
function(x) x
function(x) x
A generic model
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
generic0
Number of hyperparmeters is 1.
17001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
A generic model (type 0)
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
generic0
Number of hyperparmeters is 1.
18001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
A generic model (type 1)
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
generic1
Number of hyperparmeters is 2.
19001
log precision
prec
loggamma
1 5e-05
4
FALSE
function(x) log(x)
function(x) exp(x)
19002
beta
beta
2
FALSE
gaussian
0 0.1
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
A generic model (type 2)
FALSE
FALSE
FALSE
2
2
NULL
TRUE
TRUE
generic2
Number of hyperparmeters is 2.
20001
log precision cmatrix
prec
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
20002
log precision random
prec.random
4
FALSE
loggamma
1 0.001
function(x) log(x)
function(x) exp(x)
A generic model (type 3)
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
experimental
generic3
Number of hyperparmeters is 11.
21001
log precision1
prec1
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21002
log precision2
prec2
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21003
log precision3
prec3
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21004
log precision4
prec4
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21005
log precision5
prec5
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21006
log precision6
prec6
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21007
log precision7
prec7
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21008
log precision8
prec8
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21009
log precision9
prec9
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21010
log precision10
prec10
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
21011
log precision common
prec.common
0
TRUE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
A SPDE model
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
spde
Number of hyperparmeters is 4.
22001
theta.T
T
2
FALSE
normal
0 1
function(x) x
function(x) x
22002
theta.K
K
-2
FALSE
normal
0 1
function(x) x
function(x) x
22003
theta.KT
KT
0
FALSE
normal
0 1
function(x) x
function(x) x
22004
theta.OC
OC
-20
TRUE
normal
0 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
A SPDE2 model
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
spde2
Number of hyperparmeters is 100.
23001
theta1
t1
0
FALSE
mvnorm
1 1
function(x) x
function(x) x
23002
theta2
t2
0
FALSE
none
function(x) x
function(x) x
23003
theta3
t3
0
FALSE
none
function(x) x
function(x) x
23004
theta4
t4
0
FALSE
none
function(x) x
function(x) x
23005
theta5
t5
0
FALSE
none
function(x) x
function(x) x
23006
theta6
t6
0
FALSE
none
function(x) x
function(x) x
23007
theta7
t7
0
FALSE
none
function(x) x
function(x) x
23008
theta8
t8
0
FALSE
none
function(x) x
function(x) x
23009
theta9
t9
0
FALSE
none
function(x) x
function(x) x
23010
theta10
t10
0
FALSE
none
function(x) x
function(x) x
23011
theta11
t11
0
FALSE
none
function(x) x
function(x) x
23012
theta12
t12
0
FALSE
none
function(x) x
function(x) x
23013
theta13
t13
0
FALSE
none
function(x) x
function(x) x
23014
theta14
t14
0
FALSE
none
function(x) x
function(x) x
23015
theta15
t15
0
FALSE
none
function(x) x
function(x) x
23016
theta16
t16
0
FALSE
none
function(x) x
function(x) x
23017
theta17
t17
0
FALSE
none
function(x) x
function(x) x
23018
theta18
t18
0
FALSE
none
function(x) x
function(x) x
23019
theta19
t19
0
FALSE
none
function(x) x
function(x) x
23020
theta20
t20
0
FALSE
none
function(x) x
function(x) x
23021
theta21
t21
0
FALSE
none
function(x) x
function(x) x
23022
theta22
t22
0
FALSE
none
function(x) x
function(x) x
23023
theta23
t23
0
FALSE
none
function(x) x
function(x) x
23024
theta24
t24
0
FALSE
none
function(x) x
function(x) x
23025
theta25
t25
0
FALSE
none
function(x) x
function(x) x
23026
theta26
t26
0
FALSE
none
function(x) x
function(x) x
23027
theta27
t27
0
FALSE
none
function(x) x
function(x) x
23028
theta28
t28
0
FALSE
none
function(x) x
function(x) x
23029
theta29
t29
0
FALSE
none
function(x) x
function(x) x
23030
theta30
t30
0
FALSE
none
function(x) x
function(x) x
23031
theta31
t31
0
FALSE
none
function(x) x
function(x) x
23032
theta32
t32
0
FALSE
none
function(x) x
function(x) x
23033
theta33
t33
0
FALSE
none
function(x) x
function(x) x
23034
theta34
t34
0
FALSE
none
function(x) x
function(x) x
23035
theta35
t35
0
FALSE
none
function(x) x
function(x) x
23036
theta36
t36
0
FALSE
none
function(x) x
function(x) x
23037
theta37
t37
0
FALSE
none
function(x) x
function(x) x
23038
theta38
t38
0
FALSE
none
function(x) x
function(x) x
23039
theta39
t39
0
FALSE
none
function(x) x
function(x) x
23040
theta40
t40
0
FALSE
none
function(x) x
function(x) x
23041
theta41
t41
0
FALSE
none
function(x) x
function(x) x
23042
theta42
t42
0
FALSE
none
function(x) x
function(x) x
23043
theta43
t43
0
FALSE
none
function(x) x
function(x) x
23044
theta44
t44
0
FALSE
none
function(x) x
function(x) x
23045
theta45
t45
0
FALSE
none
function(x) x
function(x) x
23046
theta46
t46
0
FALSE
none
function(x) x
function(x) x
23047
theta47
t47
0
FALSE
none
function(x) x
function(x) x
23048
theta48
t48
0
FALSE
none
function(x) x
function(x) x
23049
theta49
t49
0
FALSE
none
function(x) x
function(x) x
23050
theta50
t50
0
FALSE
none
function(x) x
function(x) x
23051
theta51
t51
0
FALSE
none
function(x) x
function(x) x
23052
theta52
t52
0
FALSE
none
function(x) x
function(x) x
23053
theta53
t53
0
FALSE
none
function(x) x
function(x) x
23054
theta54
t54
0
FALSE
none
function(x) x
function(x) x
23055
theta55
t55
0
FALSE
none
function(x) x
function(x) x
23056
theta56
t56
0
FALSE
none
function(x) x
function(x) x
23057
theta57
t57
0
FALSE
none
function(x) x
function(x) x
23058
theta58
t58
0
FALSE
none
function(x) x
function(x) x
23059
theta59
t59
0
FALSE
none
function(x) x
function(x) x
23060
theta60
t60
0
FALSE
none
function(x) x
function(x) x
23061
theta61
t61
0
FALSE
none
function(x) x
function(x) x
23062
theta62
t62
0
FALSE
none
function(x) x
function(x) x
23063
theta63
t63
0
FALSE
none
function(x) x
function(x) x
23064
theta64
t64
0
FALSE
none
function(x) x
function(x) x
23065
theta65
t65
0
FALSE
none
function(x) x
function(x) x
23066
theta66
t66
0
FALSE
none
function(x) x
function(x) x
23067
theta67
t67
0
FALSE
none
function(x) x
function(x) x
23068
theta68
t68
0
FALSE
none
function(x) x
function(x) x
23069
theta69
t69
0
FALSE
none
function(x) x
function(x) x
23070
theta70
t70
0
FALSE
none
function(x) x
function(x) x
23071
theta71
t71
0
FALSE
none
function(x) x
function(x) x
23072
theta72
t72
0
FALSE
none
function(x) x
function(x) x
23073
theta73
t73
0
FALSE
none
function(x) x
function(x) x
23074
theta74
t74
0
FALSE
none
function(x) x
function(x) x
23075
theta75
t75
0
FALSE
none
function(x) x
function(x) x
23076
theta76
t76
0
FALSE
none
function(x) x
function(x) x
23077
theta77
t77
0
FALSE
none
function(x) x
function(x) x
23078
theta78
t78
0
FALSE
none
function(x) x
function(x) x
23079
theta79
t79
0
FALSE
none
function(x) x
function(x) x
23080
theta80
t80
0
FALSE
none
function(x) x
function(x) x
23081
theta81
t81
0
FALSE
none
function(x) x
function(x) x
23082
theta82
t82
0
FALSE
none
function(x) x
function(x) x
23083
theta83
t83
0
FALSE
none
function(x) x
function(x) x
23084
theta84
t84
0
FALSE
none
function(x) x
function(x) x
23085
theta85
t85
0
FALSE
none
function(x) x
function(x) x
23086
theta86
t86
0
FALSE
none
function(x) x
function(x) x
23087
theta87
t87
0
FALSE
none
function(x) x
function(x) x
23088
theta88
t88
0
FALSE
none
function(x) x
function(x) x
23089
theta89
t89
0
FALSE
none
function(x) x
function(x) x
23090
theta90
t90
0
FALSE
none
function(x) x
function(x) x
23091
theta91
t91
0
FALSE
none
function(x) x
function(x) x
23092
theta92
t92
0
FALSE
none
function(x) x
function(x) x
23093
theta93
t93
0
FALSE
none
function(x) x
function(x) x
23094
theta94
t94
0
FALSE
none
function(x) x
function(x) x
23095
theta95
t95
0
FALSE
none
function(x) x
function(x) x
23096
theta96
t96
0
FALSE
none
function(x) x
function(x) x
23097
theta97
t97
0
FALSE
none
function(x) x
function(x) x
23098
theta98
t98
0
FALSE
none
function(x) x
function(x) x
23099
theta99
t99
0
FALSE
none
function(x) x
function(x) x
23100
theta100
t100
0
FALSE
none
function(x) x
function(x) x
A SPDE3 model
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
spde3
Number of hyperparmeters is 100.
24001
theta1
t1
0
FALSE
mvnorm
1 1
function(x) x
function(x) x
24002
theta2
t2
0
FALSE
none
function(x) x
function(x) x
24003
theta3
t3
0
FALSE
none
function(x) x
function(x) x
24004
theta4
t4
0
FALSE
none
function(x) x
function(x) x
24005
theta5
t5
0
FALSE
none
function(x) x
function(x) x
24006
theta6
t6
0
FALSE
none
function(x) x
function(x) x
24007
theta7
t7
0
FALSE
none
function(x) x
function(x) x
24008
theta8
t8
0
FALSE
none
function(x) x
function(x) x
24009
theta9
t9
0
FALSE
none
function(x) x
function(x) x
24010
theta10
t10
0
FALSE
none
function(x) x
function(x) x
24011
theta11
t11
0
FALSE
none
function(x) x
function(x) x
24012
theta12
t12
0
FALSE
none
function(x) x
function(x) x
24013
theta13
t13
0
FALSE
none
function(x) x
function(x) x
24014
theta14
t14
0
FALSE
none
function(x) x
function(x) x
24015
theta15
t15
0
FALSE
none
function(x) x
function(x) x
24016
theta16
t16
0
FALSE
none
function(x) x
function(x) x
24017
theta17
t17
0
FALSE
none
function(x) x
function(x) x
24018
theta18
t18
0
FALSE
none
function(x) x
function(x) x
24019
theta19
t19
0
FALSE
none
function(x) x
function(x) x
24020
theta20
t20
0
FALSE
none
function(x) x
function(x) x
24021
theta21
t21
0
FALSE
none
function(x) x
function(x) x
24022
theta22
t22
0
FALSE
none
function(x) x
function(x) x
24023
theta23
t23
0
FALSE
none
function(x) x
function(x) x
24024
theta24
t24
0
FALSE
none
function(x) x
function(x) x
24025
theta25
t25
0
FALSE
none
function(x) x
function(x) x
24026
theta26
t26
0
FALSE
none
function(x) x
function(x) x
24027
theta27
t27
0
FALSE
none
function(x) x
function(x) x
24028
theta28
t28
0
FALSE
none
function(x) x
function(x) x
24029
theta29
t29
0
FALSE
none
function(x) x
function(x) x
24030
theta30
t30
0
FALSE
none
function(x) x
function(x) x
24031
theta31
t31
0
FALSE
none
function(x) x
function(x) x
24032
theta32
t32
0
FALSE
none
function(x) x
function(x) x
24033
theta33
t33
0
FALSE
none
function(x) x
function(x) x
24034
theta34
t34
0
FALSE
none
function(x) x
function(x) x
24035
theta35
t35
0
FALSE
none
function(x) x
function(x) x
24036
theta36
t36
0
FALSE
none
function(x) x
function(x) x
24037
theta37
t37
0
FALSE
none
function(x) x
function(x) x
24038
theta38
t38
0
FALSE
none
function(x) x
function(x) x
24039
theta39
t39
0
FALSE
none
function(x) x
function(x) x
24040
theta40
t40
0
FALSE
none
function(x) x
function(x) x
24041
theta41
t41
0
FALSE
none
function(x) x
function(x) x
24042
theta42
t42
0
FALSE
none
function(x) x
function(x) x
24043
theta43
t43
0
FALSE
none
function(x) x
function(x) x
24044
theta44
t44
0
FALSE
none
function(x) x
function(x) x
24045
theta45
t45
0
FALSE
none
function(x) x
function(x) x
24046
theta46
t46
0
FALSE
none
function(x) x
function(x) x
24047
theta47
t47
0
FALSE
none
function(x) x
function(x) x
24048
theta48
t48
0
FALSE
none
function(x) x
function(x) x
24049
theta49
t49
0
FALSE
none
function(x) x
function(x) x
24050
theta50
t50
0
FALSE
none
function(x) x
function(x) x
24051
theta51
t51
0
FALSE
none
function(x) x
function(x) x
24052
theta52
t52
0
FALSE
none
function(x) x
function(x) x
24053
theta53
t53
0
FALSE
none
function(x) x
function(x) x
24054
theta54
t54
0
FALSE
none
function(x) x
function(x) x
24055
theta55
t55
0
FALSE
none
function(x) x
function(x) x
24056
theta56
t56
0
FALSE
none
function(x) x
function(x) x
24057
theta57
t57
0
FALSE
none
function(x) x
function(x) x
24058
theta58
t58
0
FALSE
none
function(x) x
function(x) x
24059
theta59
t59
0
FALSE
none
function(x) x
function(x) x
24060
theta60
t60
0
FALSE
none
function(x) x
function(x) x
24061
theta61
t61
0
FALSE
none
function(x) x
function(x) x
24062
theta62
t62
0
FALSE
none
function(x) x
function(x) x
24063
theta63
t63
0
FALSE
none
function(x) x
function(x) x
24064
theta64
t64
0
FALSE
none
function(x) x
function(x) x
24065
theta65
t65
0
FALSE
none
function(x) x
function(x) x
24066
theta66
t66
0
FALSE
none
function(x) x
function(x) x
24067
theta67
t67
0
FALSE
none
function(x) x
function(x) x
24068
theta68
t68
0
FALSE
none
function(x) x
function(x) x
24069
theta69
t69
0
FALSE
none
function(x) x
function(x) x
24070
theta70
t70
0
FALSE
none
function(x) x
function(x) x
24071
theta71
t71
0
FALSE
none
function(x) x
function(x) x
24072
theta72
t72
0
FALSE
none
function(x) x
function(x) x
24073
theta73
t73
0
FALSE
none
function(x) x
function(x) x
24074
theta74
t74
0
FALSE
none
function(x) x
function(x) x
24075
theta75
t75
0
FALSE
none
function(x) x
function(x) x
24076
theta76
t76
0
FALSE
none
function(x) x
function(x) x
24077
theta77
t77
0
FALSE
none
function(x) x
function(x) x
24078
theta78
t78
0
FALSE
none
function(x) x
function(x) x
24079
theta79
t79
0
FALSE
none
function(x) x
function(x) x
24080
theta80
t80
0
FALSE
none
function(x) x
function(x) x
24081
theta81
t81
0
FALSE
none
function(x) x
function(x) x
24082
theta82
t82
0
FALSE
none
function(x) x
function(x) x
24083
theta83
t83
0
FALSE
none
function(x) x
function(x) x
24084
theta84
t84
0
FALSE
none
function(x) x
function(x) x
24085
theta85
t85
0
FALSE
none
function(x) x
function(x) x
24086
theta86
t86
0
FALSE
none
function(x) x
function(x) x
24087
theta87
t87
0
FALSE
none
function(x) x
function(x) x
24088
theta88
t88
0
FALSE
none
function(x) x
function(x) x
24089
theta89
t89
0
FALSE
none
function(x) x
function(x) x
24090
theta90
t90
0
FALSE
none
function(x) x
function(x) x
24091
theta91
t91
0
FALSE
none
function(x) x
function(x) x
24092
theta92
t92
0
FALSE
none
function(x) x
function(x) x
24093
theta93
t93
0
FALSE
none
function(x) x
function(x) x
24094
theta94
t94
0
FALSE
none
function(x) x
function(x) x
24095
theta95
t95
0
FALSE
none
function(x) x
function(x) x
24096
theta96
t96
0
FALSE
none
function(x) x
function(x) x
24097
theta97
t97
0
FALSE
none
function(x) x
function(x) x
24098
theta98
t98
0
FALSE
none
function(x) x
function(x) x
24099
theta99
t99
0
FALSE
none
function(x) x
function(x) x
24100
theta100
t100
0
FALSE
none
function(x) x
function(x) x
Gaussian random effect in dim=1 with Wishart prior
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
TRUE
iid123d
Number of hyperparmeters is 1.
25001
precision
prec
4
FALSE
wishart1d
2 1e-04
function(x) log(x)
function(x) exp(x)
Gaussian random effect in dim=2 with Wishart prior
FALSE
FALSE
TRUE
1
1 2
2
TRUE
TRUE
iid123d
Number of hyperparmeters is 3.
26001
log precision1
prec1
4
FALSE
wishart2d
4 1 1 0
function(x) log(x)
function(x) exp(x)
26002
log precision2
prec2
4
FALSE
none
function(x) log(x)
function(x) exp(x)
26003
logit correlation
cor
4
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
Gaussian random effect in dim=3 with Wishart prior
FALSE
FALSE
TRUE
1
1 2 3
3
TRUE
TRUE
iid123d
Number of hyperparmeters is 6.
27001
log precision1
prec1
4
FALSE
wishart3d
7 1 1 1 0 0 0
function(x) log(x)
function(x) exp(x)
27002
log precision2
prec2
4
FALSE
none
function(x) log(x)
function(x) exp(x)
27003
log precision3
prec3
4
FALSE
none
function(x) log(x)
function(x) exp(x)
27004
logit correlation12
cor12
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
27005
logit correlation13
cor13
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
27006
logit correlation23
cor23
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
Gaussian random effect in dim=4 with Wishart prior
FALSE
FALSE
TRUE
1
1 2 3 4
4
TRUE
TRUE
iid123d
Number of hyperparmeters is 10.
28001
log precision1
prec1
4
FALSE
wishart4d
11 1 1 1 1 0 0 0 0 0 0
function(x) log(x)
function(x) exp(x)
28002
log precision2
prec2
4
FALSE
none
function(x) log(x)
function(x) exp(x)
28003
log precision3
prec3
4
FALSE
none
function(x) log(x)
function(x) exp(x)
28004
log precision4
prec4
4
FALSE
none
function(x) log(x)
function(x) exp(x)
28005
logit correlation12
cor12
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
28006
logit correlation13
cor13
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
28007
logit correlation14
cor14
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
28008
logit correlation23
cor23
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
28009
logit correlation24
cor24
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
28010
logit correlation34
cor34
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
Gaussian random effect in dim=5 with Wishart prior
FALSE
FALSE
TRUE
1
1 2 3 4 5
5
TRUE
TRUE
iid123d
Number of hyperparmeters is 15.
29001
log precision1
prec1
4
FALSE
wishart5d
16 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
function(x) log(x)
function(x) exp(x)
29002
log precision2
prec2
4
FALSE
none
function(x) log(x)
function(x) exp(x)
29003
log precision3
prec3
4
FALSE
none
function(x) log(x)
function(x) exp(x)
29004
log precision4
prec4
4
FALSE
none
function(x) log(x)
function(x) exp(x)
29005
log precision5
prec5
4
FALSE
none
function(x) log(x)
function(x) exp(x)
29006
logit correlation12
cor12
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
29007
logit correlation13
cor13
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
29008
logit correlation14
cor14
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
29009
logit correlation15
cor15
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
29010
logit correlation23
cor23
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
29011
logit correlation24
cor24
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
29012
logit correlation25
cor25
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
29013
logit correlation34
cor34
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
29014
logit correlation35
cor35
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
29015
logit correlation45
cor45
0
FALSE
none
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
Gaussian random effect in dim=k with Wishart prior
FALSE
FALSE
TRUE
1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-1
TRUE
TRUE
iidkd
Number of hyperparmeters is 210.
29101
theta1
theta1
1048576
FALSE
wishartkd
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
function(x) x
function(x) x
29102
theta2
theta2
1048576
FALSE
none
function(x) x
function(x) x
29103
theta3
theta3
1048576
FALSE
none
function(x) x
function(x) x
29104
theta4
theta4
1048576
FALSE
none
function(x) x
function(x) x
29105
theta5
theta5
1048576
FALSE
none
function(x) x
function(x) x
29106
theta6
theta6
1048576
FALSE
none
function(x) x
function(x) x
29107
theta7
theta7
1048576
FALSE
none
function(x) x
function(x) x
29108
theta8
theta8
1048576
FALSE
none
function(x) x
function(x) x
29109
theta9
theta9
1048576
FALSE
none
function(x) x
function(x) x
29110
theta10
theta10
1048576
FALSE
none
function(x) x
function(x) x
29111
theta11
theta11
1048576
FALSE
none
function(x) x
function(x) x
29112
theta12
theta12
1048576
FALSE
none
function(x) x
function(x) x
29113
theta13
theta13
1048576
FALSE
none
function(x) x
function(x) x
29114
theta14
theta14
1048576
FALSE
none
function(x) x
function(x) x
29115
theta15
theta15
1048576
FALSE
none
function(x) x
function(x) x
29116
theta16
theta16
1048576
FALSE
none
function(x) x
function(x) x
29117
theta17
theta17
1048576
FALSE
none
function(x) x
function(x) x
29118
theta18
theta18
1048576
FALSE
none
function(x) x
function(x) x
29119
theta19
theta19
1048576
FALSE
none
function(x) x
function(x) x
29120
theta20
theta20
1048576
FALSE
none
function(x) x
function(x) x
29121
theta21
theta21
1048576
FALSE
none
function(x) x
function(x) x
29122
theta22
theta22
1048576
FALSE
none
function(x) x
function(x) x
29123
theta23
theta23
1048576
FALSE
none
function(x) x
function(x) x
29124
theta24
theta24
1048576
FALSE
none
function(x) x
function(x) x
29125
theta25
theta25
1048576
FALSE
none
function(x) x
function(x) x
29126
theta26
theta26
1048576
FALSE
none
function(x) x
function(x) x
29127
theta27
theta27
1048576
FALSE
none
function(x) x
function(x) x
29128
theta28
theta28
1048576
FALSE
none
function(x) x
function(x) x
29129
theta29
theta29
1048576
FALSE
none
function(x) x
function(x) x
29130
theta30
theta30
1048576
FALSE
none
function(x) x
function(x) x
29131
theta31
theta31
1048576
FALSE
none
function(x) x
function(x) x
29132
theta32
theta32
1048576
FALSE
none
function(x) x
function(x) x
29133
theta33
theta33
1048576
FALSE
none
function(x) x
function(x) x
29134
theta34
theta34
1048576
FALSE
none
function(x) x
function(x) x
29135
theta35
theta35
1048576
FALSE
none
function(x) x
function(x) x
29136
theta36
theta36
1048576
FALSE
none
function(x) x
function(x) x
29137
theta37
theta37
1048576
FALSE
none
function(x) x
function(x) x
29138
theta38
theta38
1048576
FALSE
none
function(x) x
function(x) x
29139
theta39
theta39
1048576
FALSE
none
function(x) x
function(x) x
29140
theta40
theta40
1048576
FALSE
none
function(x) x
function(x) x
29141
theta41
theta41
1048576
FALSE
none
function(x) x
function(x) x
29142
theta42
theta42
1048576
FALSE
none
function(x) x
function(x) x
29143
theta43
theta43
1048576
FALSE
none
function(x) x
function(x) x
29144
theta44
theta44
1048576
FALSE
none
function(x) x
function(x) x
29145
theta45
theta45
1048576
FALSE
none
function(x) x
function(x) x
29146
theta46
theta46
1048576
FALSE
none
function(x) x
function(x) x
29147
theta47
theta47
1048576
FALSE
none
function(x) x
function(x) x
29148
theta48
theta48
1048576
FALSE
none
function(x) x
function(x) x
29149
theta49
theta49
1048576
FALSE
none
function(x) x
function(x) x
29150
theta50
theta50
1048576
FALSE
none
function(x) x
function(x) x
29151
theta51
theta51
1048576
FALSE
none
function(x) x
function(x) x
29152
theta52
theta52
1048576
FALSE
none
function(x) x
function(x) x
29153
theta53
theta53
1048576
FALSE
none
function(x) x
function(x) x
29154
theta54
theta54
1048576
FALSE
none
function(x) x
function(x) x
29155
theta55
theta55
1048576
FALSE
none
function(x) x
function(x) x
29156
theta56
theta56
1048576
FALSE
none
function(x) x
function(x) x
29157
theta57
theta57
1048576
FALSE
none
function(x) x
function(x) x
29158
theta58
theta58
1048576
FALSE
none
function(x) x
function(x) x
29159
theta59
theta59
1048576
FALSE
none
function(x) x
function(x) x
29160
theta60
theta60
1048576
FALSE
none
function(x) x
function(x) x
29161
theta61
theta61
1048576
FALSE
none
function(x) x
function(x) x
29162
theta62
theta62
1048576
FALSE
none
function(x) x
function(x) x
29163
theta63
theta63
1048576
FALSE
none
function(x) x
function(x) x
29164
theta64
theta64
1048576
FALSE
none
function(x) x
function(x) x
29165
theta65
theta65
1048576
FALSE
none
function(x) x
function(x) x
29166
theta66
theta66
1048576
FALSE
none
function(x) x
function(x) x
29167
theta67
theta67
1048576
FALSE
none
function(x) x
function(x) x
29168
theta68
theta68
1048576
FALSE
none
function(x) x
function(x) x
29169
theta69
theta69
1048576
FALSE
none
function(x) x
function(x) x
29170
theta70
theta70
1048576
FALSE
none
function(x) x
function(x) x
29171
theta71
theta71
1048576
FALSE
none
function(x) x
function(x) x
29172
theta72
theta72
1048576
FALSE
none
function(x) x
function(x) x
29173
theta73
theta73
1048576
FALSE
none
function(x) x
function(x) x
29174
theta74
theta74
1048576
FALSE
none
function(x) x
function(x) x
29175
theta75
theta75
1048576
FALSE
none
function(x) x
function(x) x
29176
theta76
theta76
1048576
FALSE
none
function(x) x
function(x) x
29177
theta77
theta77
1048576
FALSE
none
function(x) x
function(x) x
29178
theta78
theta78
1048576
FALSE
none
function(x) x
function(x) x
29179
theta79
theta79
1048576
FALSE
none
function(x) x
function(x) x
29180
theta80
theta80
1048576
FALSE
none
function(x) x
function(x) x
29181
theta81
theta81
1048576
FALSE
none
function(x) x
function(x) x
29182
theta82
theta82
1048576
FALSE
none
function(x) x
function(x) x
29183
theta83
theta83
1048576
FALSE
none
function(x) x
function(x) x
29184
theta84
theta84
1048576
FALSE
none
function(x) x
function(x) x
29185
theta85
theta85
1048576
FALSE
none
function(x) x
function(x) x
29186
theta86
theta86
1048576
FALSE
none
function(x) x
function(x) x
29187
theta87
theta87
1048576
FALSE
none
function(x) x
function(x) x
29188
theta88
theta88
1048576
FALSE
none
function(x) x
function(x) x
29189
theta89
theta89
1048576
FALSE
none
function(x) x
function(x) x
29190
theta90
theta90
1048576
FALSE
none
function(x) x
function(x) x
29191
theta91
theta91
1048576
FALSE
none
function(x) x
function(x) x
29192
theta92
theta92
1048576
FALSE
none
function(x) x
function(x) x
29193
theta93
theta93
1048576
FALSE
none
function(x) x
function(x) x
29194
theta94
theta94
1048576
FALSE
none
function(x) x
function(x) x
29195
theta95
theta95
1048576
FALSE
none
function(x) x
function(x) x
29196
theta96
theta96
1048576
FALSE
none
function(x) x
function(x) x
29197
theta97
theta97
1048576
FALSE
none
function(x) x
function(x) x
29198
theta98
theta98
1048576
FALSE
none
function(x) x
function(x) x
29199
theta99
theta99
1048576
FALSE
none
function(x) x
function(x) x
29200
theta100
theta100
1048576
FALSE
none
function(x) x
function(x) x
29201
theta101
theta101
1048576
FALSE
none
function(x) x
function(x) x
29202
theta102
theta102
1048576
FALSE
none
function(x) x
function(x) x
29203
theta103
theta103
1048576
FALSE
none
function(x) x
function(x) x
29204
theta104
theta104
1048576
FALSE
none
function(x) x
function(x) x
29205
theta105
theta105
1048576
FALSE
none
function(x) x
function(x) x
29206
theta106
theta106
1048576
FALSE
none
function(x) x
function(x) x
29207
theta107
theta107
1048576
FALSE
none
function(x) x
function(x) x
29208
theta108
theta108
1048576
FALSE
none
function(x) x
function(x) x
29209
theta109
theta109
1048576
FALSE
none
function(x) x
function(x) x
29210
theta110
theta110
1048576
FALSE
none
function(x) x
function(x) x
29211
theta111
theta111
1048576
FALSE
none
function(x) x
function(x) x
29212
theta112
theta112
1048576
FALSE
none
function(x) x
function(x) x
29213
theta113
theta113
1048576
FALSE
none
function(x) x
function(x) x
29214
theta114
theta114
1048576
FALSE
none
function(x) x
function(x) x
29215
theta115
theta115
1048576
FALSE
none
function(x) x
function(x) x
29216
theta116
theta116
1048576
FALSE
none
function(x) x
function(x) x
29217
theta117
theta117
1048576
FALSE
none
function(x) x
function(x) x
29218
theta118
theta118
1048576
FALSE
none
function(x) x
function(x) x
29219
theta119
theta119
1048576
FALSE
none
function(x) x
function(x) x
29220
theta120
theta120
1048576
FALSE
none
function(x) x
function(x) x
29221
theta121
theta121
1048576
FALSE
none
function(x) x
function(x) x
29222
theta122
theta122
1048576
FALSE
none
function(x) x
function(x) x
29223
theta123
theta123
1048576
FALSE
none
function(x) x
function(x) x
29224
theta124
theta124
1048576
FALSE
none
function(x) x
function(x) x
29225
theta125
theta125
1048576
FALSE
none
function(x) x
function(x) x
29226
theta126
theta126
1048576
FALSE
none
function(x) x
function(x) x
29227
theta127
theta127
1048576
FALSE
none
function(x) x
function(x) x
29228
theta128
theta128
1048576
FALSE
none
function(x) x
function(x) x
29229
theta129
theta129
1048576
FALSE
none
function(x) x
function(x) x
29230
theta130
theta130
1048576
FALSE
none
function(x) x
function(x) x
29231
theta131
theta131
1048576
FALSE
none
function(x) x
function(x) x
29232
theta132
theta132
1048576
FALSE
none
function(x) x
function(x) x
29233
theta133
theta133
1048576
FALSE
none
function(x) x
function(x) x
29234
theta134
theta134
1048576
FALSE
none
function(x) x
function(x) x
29235
theta135
theta135
1048576
FALSE
none
function(x) x
function(x) x
29236
theta136
theta136
1048576
FALSE
none
function(x) x
function(x) x
29237
theta137
theta137
1048576
FALSE
none
function(x) x
function(x) x
29238
theta138
theta138
1048576
FALSE
none
function(x) x
function(x) x
29239
theta139
theta139
1048576
FALSE
none
function(x) x
function(x) x
29240
theta140
theta140
1048576
FALSE
none
function(x) x
function(x) x
29241
theta141
theta141
1048576
FALSE
none
function(x) x
function(x) x
29242
theta142
theta142
1048576
FALSE
none
function(x) x
function(x) x
29243
theta143
theta143
1048576
FALSE
none
function(x) x
function(x) x
29244
theta144
theta144
1048576
FALSE
none
function(x) x
function(x) x
29245
theta145
theta145
1048576
FALSE
none
function(x) x
function(x) x
29246
theta146
theta146
1048576
FALSE
none
function(x) x
function(x) x
29247
theta147
theta147
1048576
FALSE
none
function(x) x
function(x) x
29248
theta148
theta148
1048576
FALSE
none
function(x) x
function(x) x
29249
theta149
theta149
1048576
FALSE
none
function(x) x
function(x) x
29250
theta150
theta150
1048576
FALSE
none
function(x) x
function(x) x
29251
theta151
theta151
1048576
FALSE
none
function(x) x
function(x) x
29252
theta152
theta152
1048576
FALSE
none
function(x) x
function(x) x
29253
theta153
theta153
1048576
FALSE
none
function(x) x
function(x) x
29254
theta154
theta154
1048576
FALSE
none
function(x) x
function(x) x
29255
theta155
theta155
1048576
FALSE
none
function(x) x
function(x) x
29256
theta156
theta156
1048576
FALSE
none
function(x) x
function(x) x
29257
theta157
theta157
1048576
FALSE
none
function(x) x
function(x) x
29258
theta158
theta158
1048576
FALSE
none
function(x) x
function(x) x
29259
theta159
theta159
1048576
FALSE
none
function(x) x
function(x) x
29260
theta160
theta160
1048576
FALSE
none
function(x) x
function(x) x
29261
theta161
theta161
1048576
FALSE
none
function(x) x
function(x) x
29262
theta162
theta162
1048576
FALSE
none
function(x) x
function(x) x
29263
theta163
theta163
1048576
FALSE
none
function(x) x
function(x) x
29264
theta164
theta164
1048576
FALSE
none
function(x) x
function(x) x
29265
theta165
theta165
1048576
FALSE
none
function(x) x
function(x) x
29266
theta166
theta166
1048576
FALSE
none
function(x) x
function(x) x
29267
theta167
theta167
1048576
FALSE
none
function(x) x
function(x) x
29268
theta168
theta168
1048576
FALSE
none
function(x) x
function(x) x
29269
theta169
theta169
1048576
FALSE
none
function(x) x
function(x) x
29270
theta170
theta170
1048576
FALSE
none
function(x) x
function(x) x
29271
theta171
theta171
1048576
FALSE
none
function(x) x
function(x) x
29272
theta172
theta172
1048576
FALSE
none
function(x) x
function(x) x
29273
theta173
theta173
1048576
FALSE
none
function(x) x
function(x) x
29274
theta174
theta174
1048576
FALSE
none
function(x) x
function(x) x
29275
theta175
theta175
1048576
FALSE
none
function(x) x
function(x) x
29276
theta176
theta176
1048576
FALSE
none
function(x) x
function(x) x
29277
theta177
theta177
1048576
FALSE
none
function(x) x
function(x) x
29278
theta178
theta178
1048576
FALSE
none
function(x) x
function(x) x
29279
theta179
theta179
1048576
FALSE
none
function(x) x
function(x) x
29280
theta180
theta180
1048576
FALSE
none
function(x) x
function(x) x
29281
theta181
theta181
1048576
FALSE
none
function(x) x
function(x) x
29282
theta182
theta182
1048576
FALSE
none
function(x) x
function(x) x
29283
theta183
theta183
1048576
FALSE
none
function(x) x
function(x) x
29284
theta184
theta184
1048576
FALSE
none
function(x) x
function(x) x
29285
theta185
theta185
1048576
FALSE
none
function(x) x
function(x) x
29286
theta186
theta186
1048576
FALSE
none
function(x) x
function(x) x
29287
theta187
theta187
1048576
FALSE
none
function(x) x
function(x) x
29288
theta188
theta188
1048576
FALSE
none
function(x) x
function(x) x
29289
theta189
theta189
1048576
FALSE
none
function(x) x
function(x) x
29290
theta190
theta190
1048576
FALSE
none
function(x) x
function(x) x
29291
theta191
theta191
1048576
FALSE
none
function(x) x
function(x) x
29292
theta192
theta192
1048576
FALSE
none
function(x) x
function(x) x
29293
theta193
theta193
1048576
FALSE
none
function(x) x
function(x) x
29294
theta194
theta194
1048576
FALSE
none
function(x) x
function(x) x
29295
theta195
theta195
1048576
FALSE
none
function(x) x
function(x) x
29296
theta196
theta196
1048576
FALSE
none
function(x) x
function(x) x
29297
theta197
theta197
1048576
FALSE
none
function(x) x
function(x) x
29298
theta198
theta198
1048576
FALSE
none
function(x) x
function(x) x
29299
theta199
theta199
1048576
FALSE
none
function(x) x
function(x) x
29300
theta200
theta200
1048576
FALSE
none
function(x) x
function(x) x
29301
theta201
theta201
1048576
FALSE
none
function(x) x
function(x) x
29302
theta202
theta202
1048576
FALSE
none
function(x) x
function(x) x
29303
theta203
theta203
1048576
FALSE
none
function(x) x
function(x) x
29304
theta204
theta204
1048576
FALSE
none
function(x) x
function(x) x
29305
theta205
theta205
1048576
FALSE
none
function(x) x
function(x) x
29306
theta206
theta206
1048576
FALSE
none
function(x) x
function(x) x
29307
theta207
theta207
1048576
FALSE
none
function(x) x
function(x) x
29308
theta208
theta208
1048576
FALSE
none
function(x) x
function(x) x
29309
theta209
theta209
1048576
FALSE
none
function(x) x
function(x) x
29310
theta210
theta210
1048576
FALSE
none
function(x) x
function(x) x
(This model is obsolute)
FALSE
FALSE
FALSE
1
1 2
2
TRUE
TRUE
iid123d
Number of hyperparmeters is 3.
30001
log precision1
prec1
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
30002
log precision2
prec2
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
30003
correlation
cor
4
FALSE
normal
0 0.15
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
The z-model in a classical mixed model formulation
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
z
experimental
Number of hyperparmeters is 1.
31001
log precision
prec
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
Thin-plate spline model
TRUE
TRUE
FALSE
1
NULL
NULL
FALSE
TRUE
rw2d
Number of hyperparmeters is 1.
32001
log precision
prec
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
Thin-plate spline with iid noise
TRUE
TRUE
TRUE
2
2
NULL
FALSE
TRUE
experimental
rw2diid
Number of hyperparmeters is 2.
33001
log precision
prec
pc.prec
1 0.01
4
FALSE
function(x) log(x)
function(x) exp(x)
33002
logit phi
phi
pc
0.5 0.5
3
FALSE
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Spatial lag model
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
slm
experimental
Number of hyperparmeters is 2.
34001
log precision
prec
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
34002
rho
rho
0
FALSE
normal
0 10
function(x) log(x / (1 - x))
function(x) 1 / (1 + exp(-x))
Matern covariance function on a regular grid
FALSE
TRUE
FALSE
1
NULL
NULL
FALSE
TRUE
matern2d
Number of hyperparmeters is 2.
35001
log precision
prec
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
35002
log range
range
2
FALSE
loggamma
1 0.01
function(x) log(x)
function(x) exp(x)
Dense Matern field
FALSE
FALSE
FALSE
1
NULL
NULL
TRUE
TRUE
experimental
dmatern
Number of hyperparmeters is 3.
35101
log precision
prec
3
FALSE
pc.prec
1 0.01
function(x) log(x)
function(x) exp(x)
35102
log range
range
0
FALSE
pc.range
1 0.5
function(x) log(x)
function(x) exp(x)
35103
log nu
nu
-0.693147180559945
TRUE
loggamma
0.5 1
function(x) log(x)
function(x) exp(x)
Create a copy of a model component
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
copy
Number of hyperparmeters is 1.
36001
beta
b
0
TRUE
normal
1 10
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") } }
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") } }
Create a scopy of a model component
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
experimental
scopy
Number of hyperparmeters is 15.
36101
mean
mean
1
FALSE
normal
1 10
function(x) x
function(x) x
36102
slope
slope
0
FALSE
normal
0 10
function(x) x
function(x) x
36103
spline.theta1
spline
0
FALSE
laplace
0 10
function(x) x
function(x) x
36104
spline.theta2
spline2
0
FALSE
none
function(x) x
function(x) x
36105
spline.theta3
spline3
0
FALSE
none
function(x) x
function(x) x
36106
spline.theta4
spline4
0
FALSE
none
function(x) x
function(x) x
36107
spline.theta5
spline5
0
FALSE
none
function(x) x
function(x) x
36108
spline.theta6
spline6
0
FALSE
none
function(x) x
function(x) x
36109
spline.theta7
spline7
0
FALSE
none
function(x) x
function(x) x
36110
spline.theta8
spline8
0
FALSE
none
function(x) x
function(x) x
36111
spline.theta9
spline9
0
FALSE
none
function(x) x
function(x) x
36112
spline.theta10
spline10
0
FALSE
none
function(x) x
function(x) x
36113
spline.theta11
spline11
0
FALSE
none
function(x) x
function(x) x
36114
spline.theta12
spline12
0
FALSE
none
function(x) x
function(x) x
36115
spline.theta13
spline13
0
FALSE
none
function(x) x
function(x) x
Constrained linear effect
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
clinear
Number of hyperparmeters is 1.
37001
beta
b
1
FALSE
normal
1 10
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") } }
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") } }
Sigmoidal effect of a covariate
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
experimental
sigm
Number of hyperparmeters is 3.
38001
beta
b
1
FALSE
normal
1 10
function(x) x
function(x) x
38002
loghalflife
halflife
3
FALSE
loggamma
3 1
function(x) log(x)
function(x) exp(x)
38003
logshape
shape
0
FALSE
loggamma
10 10
function(x) log(x)
function(x) exp(x)
Reverse sigmoidal effect of a covariate
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
experimental
sigm
Number of hyperparmeters is 3.
39001
beta
b
1
FALSE
normal
1 10
function(x) x
function(x) x
39002
loghalflife
halflife
3
FALSE
loggamma
3 1
function(x) log(x)
function(x) exp(x)
39003
logshape
shape
0
FALSE
loggamma
10 10
function(x) log(x)
function(x) exp(x)
A nonlinear model of a covariate
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
experimental
log1exp
Number of hyperparmeters is 3.
39011
beta
b
1
FALSE
normal
0 1
function(x) x
function(x) x
39012
alpha
a
0
FALSE
normal
0 1
function(x) x
function(x) x
39013
gamma
g
0
FALSE
normal
0 1
function(x) x
function(x) x
A nonlinear model of a covariate
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
experimental
logdist
Number of hyperparmeters is 3.
39021
beta
b
1
FALSE
normal
0 1
function(x) x
function(x) x
39022
alpha1
a1
0
FALSE
loggamma
0.1 1
function(x) log(x)
function(x) exp(x)
39023
alpha2
a2
0
FALSE
loggamma
0.1 1
function(x) log(x)
function(x) exp(x)
Valid models in this section are:
Exchangeable correlations
Number of hyperparmeters is 1.
40001
logit correlation
rho
1
FALSE
normal
0 0.2
function(x, REPLACE.ME.ngroup) log((1 + x * (ngroup - 1)) / (1 - x))
function(x, REPLACE.ME.ngroup) (exp(x) - 1) / (exp(x) + ngroup - 1)
Exchangeable positive correlations
Number of hyperparmeters is 1.
40101
logit correlation
rho
1
FALSE
pc.cor0
0.5 0.5
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
AR(1) correlations
Number of hyperparmeters is 1.
41001
logit correlation
rho
2
FALSE
normal
0 0.15
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
AR(p) correlations
Number of hyperparmeters is 11.
42001
log precision
prec
0
TRUE
pc.prec
3 0.01
function(x) log(x)
function(x) exp(x)
42002
pacf1
pacf1
2
FALSE
pc.cor0
0.5 0.5
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
42003
pacf2
pacf2
0
FALSE
pc.cor0
0.5 0.4
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
42004
pacf3
pacf3
0
FALSE
pc.cor0
0.5 0.3
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
42005
pacf4
pacf4
0
FALSE
pc.cor0
0.5 0.2
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
42006
pacf5
pacf5
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
42007
pacf6
pacf6
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
42008
pacf7
pacf7
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
42009
pacf8
pacf8
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
42010
pacf9
pacf9
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
42011
pacf10
pacf10
0
FALSE
pc.cor0
0.5 0.1
function(x) log((1 + x) / (1 - x))
function(x) 2 * exp(x) / (1 + exp(x)) - 1
Random walk of order 1
Number of hyperparmeters is 1.
43001
log precision
prec
loggamma
1 5e-05
0
TRUE
function(x) log(x)
function(x) exp(x)
Random walk of order 2
Number of hyperparmeters is 1.
44001
log precision
prec
loggamma
1 5e-05
0
TRUE
function(x) log(x)
function(x) exp(x)
Besag model
Number of hyperparmeters is 1.
45001
log precision
prec
loggamma
1 5e-05
0
TRUE
function(x) log(x)
function(x) exp(x)
Independent model
Number of hyperparmeters is 1.
46001
log precision
prec
loggamma
1 5e-05
0
TRUE
function(x) log(x)
function(x) exp(x)
Valid models in this section are:
Random walk of order 1
Number of hyperparmeters is 0.
Random walk of order 2
Number of hyperparmeters is 0.
Valid models in this section are:
Gaussian mixture
Number of hyperparmeters is 1.
47001
log precision
prec
Precision for the Gaussian observations
Log precision for the Gaussian observations
pc.prec
1 0.01
0
FALSE
function(x) log(x)
function(x) exp(x)
LogGamma mixture
Number of hyperparmeters is 1.
47101
log precision
prec
pc.mgamma
4.8
4
FALSE
function(x) log(x)
function(x) exp(x)
Minus-LogGamma mixture
Number of hyperparmeters is 1.
47201
log precision
prec
pc.mgamma
4.8
4
FALSE
function(x) log(x)
function(x) exp(x)
Valid models in this section are:
The default link
Number of hyperparmeters is 0.
The complementary log-log link
Number of hyperparmeters is 0.
The complement complementary log-log link
Number of hyperparmeters is 0.
The log-log link
Number of hyperparmeters is 0.
The identity link
Number of hyperparmeters is 0.
The inverse link
Number of hyperparmeters is 0.
The log-link
Number of hyperparmeters is 0.
The loga-link
Number of hyperparmeters is 0.
The negative log-link
Number of hyperparmeters is 0.
The logit-link
Number of hyperparmeters is 0.
The probit-link
Number of hyperparmeters is 0.
The cauchit-link
Number of hyperparmeters is 0.
The tan-link
Number of hyperparmeters is 0.
The quantile-link
Number of hyperparmeters is 0.
The population quantile-link
Number of hyperparmeters is 0.
Logit link with sensitivity and specificity
disabled
NA
Number of hyperparmeters is 2.
48001
sensitivity
sens
logitbeta
10 5
1
FALSE
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
48002
specificity
spec
logitbeta
10 5
1
FALSE
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Log-link with an offset
logoffset
Number of hyperparmeters is 1.
49001
beta
b
normal
0 100
0
TRUE
function(x) log(x)
function(x) exp(x)
Logit-link with an offset
experimental
logitoffset
Number of hyperparmeters is 1.
49011
prob
p
normal
-1 100
-1
FALSE
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Robit link
experimental
robit
Number of hyperparmeters is 1.
49021
log degrees of freedom
dof
1.6094379124341
TRUE
pc.dof
50 0.5
function(x) log(x - 2)
function(x) 2 + exp(x)
Skew-normal link
linksn
Number of hyperparmeters is 2.
49031
skewness
skew
0.00123456789
FALSE
pc.sn
10
function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))
function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)
49032
intercept
intercept
0
FALSE
linksnintercept
0 0
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
GEV link
experimental
gev
Number of hyperparmeters is 2.
49033
tail
xi
-3
FALSE
pc.gevtail
7 0 0.5
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))
49034
intercept
intercept
0
FALSE
normal
0 1
function(x) log(x / (1 - x))
function(x) 1 / (1 + exp(-x))
Complement GEV link
experimental
cgev
Number of hyperparmeters is 2.
49035
tail
xi
-3
FALSE
pc.gevtail
7 0 0.5
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))
49036
intercept
intercept
0
FALSE
normal
0 1
function(x) log(x / (1 - x))
function(x) 1 / (1 + exp(-x))
Power logit link
powerlogit
Number of hyperparmeters is 2.
49131
power
power
0.00123456789
FALSE
normal
0 10
function(x) log(x)
function(x) exp(x)
49132
intercept
intercept
0
FALSE
logitbeta
1 1
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
A test1-link function (experimental)
NA
Number of hyperparmeters is 1.
50001
beta
b
normal
0 100
0
FALSE
function(x) x
function(x) x
A special1-link function (experimental)
NA
Number of hyperparmeters is 11.
51001
log precision
prec
0
FALSE
loggamma
1 1
function(x) x
function(x) x
51002
beta1
beta1
0
FALSE
mvnorm
0 100
function(x) x
function(x) x
51003
beta2
beta2
0
FALSE
none
function(x) x
function(x) x
51004
beta3
beta3
0
FALSE
none
function(x) x
function(x) x
51005
beta4
beta4
0
FALSE
none
function(x) x
function(x) x
51006
beta5
beta5
0
FALSE
none
function(x) x
function(x) x
51007
beta6
beta6
0
FALSE
none
function(x) x
function(x) x
51008
beta7
beta7
0
FALSE
none
function(x) x
function(x) x
51009
beta8
beta8
0
FALSE
none
function(x) x
function(x) x
51010
beta9
beta9
0
FALSE
none
function(x) x
function(x) x
51011
beta10
beta10
0
FALSE
none
function(x) x
function(x) x
A special2-link function (experimental)
NA
Number of hyperparmeters is 1.
52001
beta
b
normal
0 10
0
FALSE
function(x) x
function(x) x
Valid models in this section are:
(do not use)
Number of hyperparmeters is 1.
53001
log precision
prec
13.8155105579643
TRUE
loggamma
1 1e-05
function(x) log(x)
function(x) exp(x)
Valid models in this section are:
A random walk of order 1 for the log-hazard
Number of hyperparmeters is 1.
54001
log precision
prec
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
A random walk of order 2 for the log-hazard
Number of hyperparmeters is 1.
55001
log precision
prec
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
An iid model for the log-hazard
Number of hyperparmeters is 1.
55501
log precision
prec
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
Valid models in this section are:
The Poisson likelihood
FALSE
TRUE
default log logoffset quantile test1 special1 special2
poisson
Number of hyperparmeters is 0.
The nzPoisson likelihood
FALSE
TRUE
default log logoffset
nzpoisson
Number of hyperparmeters is 0.
The Poisson likelihood (expert version)
FALSE
TRUE
default log logoffset quantile test1 special1 special2
poisson
Number of hyperparmeters is 0.
Then censored Poisson likelihood
FALSE
TRUE
default log logoffset test1 special1 special2
cenpoisson
Number of hyperparmeters is 0.
Then censored Poisson likelihood (version 2)
FALSE
TRUE
default log logoffset test1 special1 special2
cenpoisson2
Number of hyperparmeters is 0.
The generalized Poisson likelihood
FALSE
TRUE
default log logoffset
gpoisson
experimental
Number of hyperparmeters is 2.
56001
overdispersion
phi
Overdispersion for gpoisson
Log overdispersion for gpoisson
0
FALSE
loggamma
1 1
function(x) log(x)
function(x) exp(x)
56002
p
p
Parameter p for gpoisson
Parameter p_intern for gpoisson
1
TRUE
normal
1 100
function(x) x
function(x) x
The Poisson.special1 likelihood
FALSE
TRUE
default log
poisson-special
Number of hyperparmeters is 1.
56100
logit probability
prob
one-probability parameter for poisson.special1
intern one-probability parameter for poisson.special1
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
New 0-inflated Poisson
experimental
FALSE
TRUE
default log quantile
default logit cauchit probit cloglog ccloglog
0inflated
Number of hyperparmeters is 10.
56201
beta1
beta1
beta1 for 0poisson observations
beta1 for 0poisson observations
-4
FALSE
normal
-4 10
function(x) x
function(x) x
56202
beta2
beta2
beta2 for 0poisson observations
beta2 for 0poisson observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56203
beta3
beta3
beta3 for 0poisson observations
beta3 for 0poisson observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56204
beta4
beta4
beta4 for 0poisson observations
beta4 for 0poisson observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56205
beta5
beta5
beta5 for 0poisson observations
beta5 for 0poisson observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56206
beta6
beta6
beta6 for 0poisson observations
beta6 for 0poisson observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56207
beta7
beta7
beta7 for 0poisson observations
beta7 for 0poisson observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56208
beta8
beta8
beta8 for 0poisson observations
beta8 for 0poisson observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56209
beta9
beta9
beta9 for 0poisson observations
beta9 for 0poisson observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56210
beta10
beta10
beta10 for 0poisson observations
beta10 for 0poisson observations
0
FALSE
normal
0 10
function(x) x
function(x) x
New 0-inflated Poisson Swap
experimental
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit
default log
0inflated
Number of hyperparmeters is 10.
56301
beta1
beta1
beta1 for 0poissonS observations
beta1 for 0poissonS observations
-4
FALSE
normal
-4 10
function(x) x
function(x) x
56302
beta2
beta2
beta2 for 0poissonS observations
beta2 for 0poissonS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56303
beta3
beta3
beta3 for 0poissonS observations
beta3 for 0poissonS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56304
beta4
beta4
beta4 for 0poissonS observations
beta4 for 0poissonS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56305
beta5
beta5
beta5 for 0poissonS observations
beta5 for 0poissonS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56306
beta6
beta6
beta6 for 0poissonS observations
beta6 for 0poissonS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56307
beta7
beta7
beta7 for 0poissonS observations
beta7 for 0poissonS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56308
beta8
beta8
beta8 for 0poissonS observations
beta8 for 0poissonS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56309
beta9
beta9
beta9 for 0poissonS observations
beta9 for 0poissonS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56310
beta10
beta10
beta10 for 0poissonS observations
beta10 for 0poissonS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
The Bell likelihood
experimental
FALSE
TRUE
default log
bell
Number of hyperparmeters is 0.
New 0-inflated Binomial
experimental
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog log
default logit cauchit probit cloglog ccloglog
0inflated
Number of hyperparmeters is 10.
56401
beta1
beta1
beta1 for 0binomial observations
beta1 for 0binomial observations
-4
FALSE
normal
-4 10
function(x) x
function(x) x
56402
beta2
beta2
beta2 for 0binomial observations
beta2 for 0binomial observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56403
beta3
beta3
beta3 for 0binomial observations
beta3 for 0binomial observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56404
beta4
beta4
beta4 for 0binomial observations
beta4 for 0binomial observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56405
beta5
beta5
beta5 for 0binomial observations
beta5 for 0binomial observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56406
beta6
beta6
beta6 for 0binomial observations
beta6 for 0binomial observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56407
beta7
beta7
beta7 for 0binomial observations
beta7 for 0binomial observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56408
beta8
beta8
beta8 for 0binomial observations
beta8 for 0binomial observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56409
beta9
beta9
beta9 for 0binomial observations
beta9 for 0binomial observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56410
beta10
beta10
beta10 for 0binomial observations
beta10 for 0binomial observations
0
FALSE
normal
0 10
function(x) x
function(x) x
New 0-inflated Binomial Swap
experimental
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog log
default logit cauchit probit cloglog ccloglog
0inflated
Number of hyperparmeters is 10.
56501
beta1
beta1
beta1 for 0binomialS observations
beta1 for 0binomialS observations
-4
FALSE
normal
-4 10
function(x) x
function(x) x
56502
beta2
beta2
beta2 for 0binomialS observations
beta2 for 0binomialS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56503
beta3
beta3
beta3 for 0binomialS observations
beta3 for 0binomialS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56504
beta4
beta4
beta4 for 0binomialS observations
beta4 for 0binomialS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56505
beta5
beta5
beta5 for 0binomialS observations
beta5 for 0binomialS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56506
beta6
beta6
beta6 for 0binomialS observations
beta6 for 0binomialS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56507
beta7
beta7
beta7 for 0binomialS observations
beta7 for 0binomialS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56508
beta8
beta8
beta8 for 0binomialS observations
beta8 for 0binomialS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56509
beta9
beta9
beta9 for 0binomialS observations
beta9 for 0binomialS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
56510
beta10
beta10
beta10 for 0binomialS observations
beta10 for 0binomialS observations
0
FALSE
normal
0 10
function(x) x
function(x) x
The Binomial likelihood
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit gev cgev
binomial
Number of hyperparmeters is 0.
The Binomial likelihood (expert version)
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit gev cgev
binomial
experimental
Number of hyperparmeters is 0.
Likelihood for the proportional odds model
experimental
FALSE
TRUE
default identity
pom
Number of hyperparmeters is 10.
57101
theta1
theta1
theta1 for POM
theta1 for POM
NA
FALSE
dirichlet
3
function(x) x
function(x) x
57102
theta2
theta2
theta2 for POM
theta2 for POM
NA
FALSE
none
function(x) log(x)
function(x) exp(x)
57103
theta3
theta3
theta3 for POM
theta3 for POM
NA
FALSE
none
function(x) log(x)
function(x) exp(x)
57104
theta4
theta4
theta4 for POM
theta4 for POM
NA
FALSE
none
function(x) log(x)
function(x) exp(x)
57105
theta5
theta5
theta5 for POM
theta5 for POM
NA
FALSE
none
function(x) log(x)
function(x) exp(x)
57106
theta6
theta6
theta6 for POM
theta6 for POM
NA
FALSE
none
function(x) log(x)
function(x) exp(x)
57107
theta7
theta7
theta7 for POM
theta7 for POM
NA
FALSE
none
function(x) log(x)
function(x) exp(x)
57108
theta8
theta8
theta8 for POM
theta8 for POM
NA
FALSE
none
function(x) log(x)
function(x) exp(x)
57109
theta9
theta9
theta9 for POM
theta9 for POM
NA
FALSE
none
function(x) log(x)
function(x) exp(x)
57110
theta10
theta10
theta10 for POM
theta10 for POM
NA
FALSE
none
function(x) log(x)
function(x) exp(x)
The blended Generalized Extreme Value likelihood
experimental
FALSE
FALSE
default identity log
bgev
Number of hyperparmeters is 12.
57201
spread
sd
spread for BGEV observations
log spread for BGEV observations
0
FALSE
loggamma
1 3
function(x) log(x)
function(x) exp(x)
57202
tail
xi
tail for BGEV observations
intern tail for BGEV observations
-4
FALSE
pc.gevtail
7 0 0.5
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))
57203
beta1
beta1
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
57204
beta2
beta2
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
57205
beta3
beta3
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
57206
beta4
beta4
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
57207
beta5
beta5
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
57208
beta6
beta6
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
57209
beta7
beta7
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
57210
beta8
beta8
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
57211
beta9
beta9
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
57212
beta10
beta
MUST BE FIXED
MUST BE FIXED
NA
FALSE
normal
0 300
function(x) x
function(x) x
The Gamma likelihood
FALSE
FALSE
default log quantile
gamma
Number of hyperparmeters is 1.
58001
precision parameter
prec
Precision-parameter for the Gamma observations
Intern precision-parameter for the Gamma observations
4.60517018598809
FALSE
loggamma
1 0.01
function(x) log(x)
function(x) exp(x)
The Gamma likelihood (survival)
TRUE
FALSE
experimental
default log neglog quantile
gammasurv
Number of hyperparmeters is 11.
58101
precision parameter
prec
Precision-parameter for the Gamma surv observations
Intern precision-parameter for the Gamma surv observations
0
FALSE
loggamma
1 0.01
function(x) log(x)
function(x) exp(x)
58102
beta1
beta1
beta1 for Gamma-Cure
beta1 for Gamma-Cure
-7
FALSE
normal
-4 100
function(x) x
function(x) x
58103
beta2
beta2
beta2 for Gamma-Cure
beta2 for Gamma-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58104
beta3
beta3
beta3 for Gamma-Cure
beta3 for Gamma-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58105
beta4
beta4
beta4 for Ga mma-Cure
beta4 for Gamma-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58106
beta5
beta5
beta5 for Gamma-Cure
beta5 for Gamma-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58107
beta6
beta6
beta6 for Gamma-Cure
beta6 for Gamma-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58108
beta7
beta7
beta7 for Gamma-Cure
beta7 for Gamma-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58109
beta8
beta8
beta8 for Gamma-Cure
beta8 for Gamma-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58110
beta9
beta9
beta9 for Gamma-Cure
beta9 for Gamma-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58111
beta10
beta10
beta10 for Gamma-Cure
beta10 for Gamma-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
A special case of the Gamma likelihood
FALSE
FALSE
default log neglog
gammajw
Number of hyperparmeters is 0.
A special case of the Gamma likelihood (survival)
TRUE
FALSE
default log
gammajw
Number of hyperparmeters is 10.
58200
beta1
beta1
beta1 for GammaJW-Cure
beta1 for GammaJW-Cure
-7
FALSE
normal
-4 100
function(x) x
function(x) x
58201
beta2
beta2
beta1 for GammaJW-Cure
beta1 for GammaJW-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58202
beta3
beta3
beta3 for GammaJW-Cure
beta3 for GammaJW-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58203
beta4
beta4
beta4 for GammaJW-Cure
beta4 for GammaJW-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58204
beta5
beta5
beta5 for GammaJW-Cure
beta5 for GammaJW-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58205
beta6
beta6
beta6 for GammaJW-Cure
beta6 for GammaJW-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58206
beta7
beta7
beta7 for GammaJW-Cure
beta7 for GammaJW-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58207
beta8
beta8
beta8 for GammaJW-Cure
beta8 for GammaJW-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58208
beta9
beta9
beta9 for GammaJW-Cure
beta9 for GammaJW-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
58209
beta10
beta10
beta10 for GammaJW-Cure
beta10 for GammaJW-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
A Gamma generalisation of the Poisson likelihood
FALSE
FALSE
default log
experimental
gammacount
Number of hyperparmeters is 1.
59001
log alpha
alpha
Log-alpha parameter for Gammacount observations
Alpha parameter for Gammacount observations
0
FALSE
pc.gammacount
3
function(x) log(x)
function(x) exp(x)
A quantile version of the Kumar likelihood
FALSE
FALSE
default logit loga cauchit
qkumar
Number of hyperparmeters is 1.
60001
precision parameter
prec
precision for qkumar observations
log precision for qkumar observations
1
FALSE
loggamma
1 0.1
function(x, sc = 0.1) log(x) / sc
function(x, sc = 0.1) exp(sc * x)
A quantile loglogistic likelihood
FALSE
FALSE
default log neglog
qloglogistic
Number of hyperparmeters is 1.
60011
log alpha
alpha
alpha for qloglogistic observations
log alpha for qloglogistic observations
1
FALSE
loggamma
25 25
function(x) log(x)
function(x) exp(x)
A quantile loglogistic likelihood (survival)
TRUE
FALSE
default log neglog
qloglogistic
Number of hyperparmeters is 11.
60021
log alpha
alpha
alpha for qloglogisticsurv observations
log alpha for qloglogisticsurv observations
1
FALSE
loggamma
25 25
function(x) log(x)
function(x) exp(x)
60022
beta1
beta1
beta1 for qlogLogistic-Cure
beta1 for logLogistic-Cure
-5
FALSE
normal
-4 100
function(x) x
function(x) x
60023
beta2
beta2
beta2 for qlogLogistic-Cure
beta2 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
60024
beta3
beta3
beta3 for qlogLogistic-Cure
beta3 for qlogLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
60025
beta4
beta4
beta4 for qlogLogistic-Cure
beta4 for qlogLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
60026
beta5
beta5
beta5 for qlogLogistic-Cure
beta5 for qlogLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
60027
beta6
beta6
beta6 for qlogLogistic-Cure
beta6 for qlogLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
60028
beta7
beta7
beta7 for qlogLogistic-Cure
beta7 for qlogLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
60029
beta8
beta8
beta8 for qlogLogistic-Cure
beta8 for qlogLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
60030
beta9
beta9
beta9 for qlogLogistic-Cure
beta9 for qlogLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
60031
beta10
beta10
beta10 for qlogLogistic-Cure
beta10 for qlogLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
The Beta likelihood
FALSE
FALSE
default logit loga cauchit probit cloglog ccloglog loglog
beta
Number of hyperparmeters is 1.
61001
precision parameter
phi
precision parameter for the beta observations
intern precision-parameter for the beta observations
2.30258509299405
FALSE
loggamma
1 0.1
function(x) log(x)
function(x) exp(x)
The Beta-Binomial likelihood
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
betabinomial
Number of hyperparmeters is 1.
62001
overdispersion
rho
overdispersion for the betabinomial observations
intern overdispersion for the betabinomial observations
0
FALSE
gaussian
0 0.4
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
The Beta-Binomial Normal approximation likelihood
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
betabinomialna
Number of hyperparmeters is 1.
62101
overdispersion
rho
overdispersion for the betabinomialna observations
intern overdispersion for the betabinomialna observations
0
FALSE
gaussian
0 0.4
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
The clustered Binomial likelihood
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
experimental
cbinomial
Number of hyperparmeters is 0.
The negBinomial likelihood
FALSE
TRUE
default log logoffset quantile
nbinomial
Number of hyperparmeters is 1.
63001
size
size
size for the nbinomial observations (1/overdispersion)
log size for the nbinomial observations (1/overdispersion)
2.30258509299405
FALSE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
The negBinomial2 likelihood
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog
nbinomial
Number of hyperparmeters is 0.
The CenNegBinomial2 likelihood (similar to cenpoisson2)
experimental
FALSE
TRUE
default log logoffset quantile
cennbinomial2
Number of hyperparmeters is 1.
63101
size
size
size for the cennbinomial2 observations (1/overdispersion)
log size for the cennbinomial2 observations (1/overdispersion)
2.30258509299405
FALSE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
The simplex likelihood
FALSE
FALSE
default logit loga cauchit probit cloglog ccloglog loglog
simplex
Number of hyperparmeters is 1.
64001
log precision
prec
Precision for the Simplex observations
Log precision for the Simplex observations
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
The Gaussian likelihoood
FALSE
FALSE
default identity logit loga cauchit log logoffset
gaussian
Number of hyperparmeters is 2.
65001
log precision
prec
Precision for the Gaussian observations
Log precision for the Gaussian observations
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
65002
log precision offset
precoffset
NOT IN USE
NOT IN USE
72.0873067782343
TRUE
none
function(x) log(x)
function(x) exp(x)
The stdGaussian likelihoood
FALSE
FALSE
default identity logit loga cauchit log logoffset
gaussian
Number of hyperparmeters is 0.
The GaussianJW likelihoood
experimental
FALSE
FALSE
default logit probit
gaussianjw
Number of hyperparmeters is 3.
65101
beta1
beta1
beta1 for GaussianJW observations
beta1 for GaussianJW observations
0
FALSE
normal
0 100
function(x) x
function(x) x
65102
beta2
beta2
beta2 for GaussianJW observations
beta2 for GaussianJW observations
1
FALSE
normal
1 100
function(x) x
function(x) x
65103
beta3
beta3
beta3 for GaussianJW observations
beta3 for GaussianJW observations
-1
FALSE
normal
-1 100
function(x) x
function(x) x
The aggregated Gaussian likelihoood
experimental
FALSE
FALSE
default identity logit loga cauchit log logoffset
agaussian
Number of hyperparmeters is 1.
66001
log precision
prec
Precision for the AggGaussian observations
Log precision for the AggGaussian observations
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
Generalized Gaussian
experimental
FALSE
TRUE
default identity
default log
ggaussian
Number of hyperparmeters is 10.
66501
beta1
beta1
beta1 for ggaussian observations
beta1 for ggaussian observations
4
FALSE
normal
9.33 0.61
function(x) x
function(x) x
66502
beta2
beta2
beta2 for ggaussian observations
beta2 for ggaussian observations
0
FALSE
normal
0 10
function(x) x
function(x) x
66503
beta3
beta3
beta3 for ggaussian observations
beta3 for ggaussian observations
0
FALSE
normal
0 10
function(x) x
function(x) x
66504
beta4
beta4
beta4 for ggaussian observations
beta4 for ggaussian observations
0
FALSE
normal
0 10
function(x) x
function(x) x
66505
beta5
beta5
beta5 for ggaussian observations
beta5 for ggaussian observations
0
FALSE
normal
0 10
function(x) x
function(x) x
66506
beta6
beta6
beta6 for ggaussian observations
beta6 for ggaussian observations
0
FALSE
normal
0 10
function(x) x
function(x) x
66507
beta7
beta7
beta7 for ggaussian observations
beta7 for ggaussian observations
0
FALSE
normal
0 10
function(x) x
function(x) x
66508
beta8
beta8
beta8 for ggaussian observations
beta8 for ggaussian observations
0
FALSE
normal
0 10
function(x) x
function(x) x
66509
beta9
beta9
beta9 for ggaussian observations
beta9 for ggaussian observations
0
FALSE
normal
0 10
function(x) x
function(x) x
66510
beta10
beta10
beta10 for ggaussian observations
beta10 for ggaussian observations
0
FALSE
normal
0 10
function(x) x
function(x) x
Generalized GaussianS
experimental
FALSE
TRUE
default log
default identity
ggaussian
Number of hyperparmeters is 10.
66601
beta1
beta1
beta1 for ggaussianS observations
beta1 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
66602
beta2
beta2
beta2 for ggaussianS observations
beta2 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
66603
beta3
beta3
beta3 for ggaussianS observations
beta3 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
66604
beta4
beta4
beta4 for ggaussianS observations
beta4 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
66605
beta5
beta5
beta5 for ggaussianS observations
beta5 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
66606
beta6
beta6
beta6 for ggaussianS observations
beta6 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
66607
beta7
beta7
beta7 for ggaussianS observations
beta7 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
66608
beta8
beta8
beta8 for ggaussianS observations
beta8 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
66609
beta9
beta9
beta9 for ggaussianS observations
beta9 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
66610
beta10
beta10
beta10 for ggaussianS observations
beta10 for ggaussianS observations
0
FALSE
normal
0 0.001
function(x) x
function(x) x
The circular Gaussian likelihoood
FALSE
FALSE
default tan
circular-normal
experimental
Number of hyperparmeters is 1.
67001
log precision parameter
prec
Precision parameter for the Circular Normal observations
Log precision parameter for the Circular Normal observations
2
FALSE
loggamma
1 0.01
function(x) log(x)
function(x) exp(x)
The wrapped Cauchy likelihoood
FALSE
FALSE
default tan
wrapped-cauchy
disabled
Number of hyperparmeters is 1.
68001
log precision parameter
prec
Precision parameter for the Wrapped Cauchy observations
Log precision parameter for the Wrapped Cauchy observations
2
FALSE
loggamma
1 0.005
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
(experimental)
FALSE
FALSE
default identity
iidgamma
experimental
Number of hyperparmeters is 2.
69001
logshape
shape
Shape parameter for iid-gamma
Log shape parameter for iid-gamma
0
FALSE
loggamma
100 100
function(x) log(x)
function(x) exp(x)
69002
lograte
rate
Rate parameter for iid-gamma
Log rate parameter for iid-gamma
0
FALSE
loggamma
100 100
function(x) log(x)
function(x) exp(x)
(experimental)
FALSE
FALSE
default logit loga
iidlogitbeta
experimental
Number of hyperparmeters is 2.
70001
log.a
a
a parameter for iid-beta
Log a parameter for iid-beta
1
FALSE
loggamma
1 1
function(x) log(x)
function(x) exp(x)
70002
log.b
b
Rate parameter for iid-gamma
Log rate parameter for iid-gamma
1
FALSE
loggamma
1 1
function(x) log(x)
function(x) exp(x)
(experimental)
FALSE
FALSE
default identity
loggammafrailty
experimental
Number of hyperparmeters is 1.
71001
log precision
prec
precision for the gamma frailty
log precision for the gamma frailty
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
The Logistic likelihoood
FALSE
FALSE
default identity
logistic
Number of hyperparmeters is 1.
72001
log precision
prec
precision for the logistic observations
log precision for the logistic observations
1
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
The Skew-Normal likelihoood
experimental
FALSE
FALSE
default identity
sn
Number of hyperparmeters is 2.
74001
log precision
prec
precision for skew-normal observations
log precision for skew-normal observations
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
74002
logit skew
skew
Skewness for skew-normal observations
Intern skewness for skew-normal observations
0.00123456789
FALSE
pc.sn
10
function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))
function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)
The Generalized Extreme Value likelihood
FALSE
FALSE
default identity
disabled: Use likelihood model 'bgev' instead; see inla.doc('bgev')
gev
Number of hyperparmeters is 2.
76001
log precision
prec
precision for GEV observations
log precision for GEV observations
4
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
76002
tail parameter
tail
tail parameter for GEV observations
tail parameter for GEV observations
0
FALSE
gaussian
0 25
function(x) x
function(x) x
The log-Normal likelihood
FALSE
FALSE
default identity
lognormal
Number of hyperparmeters is 1.
77101
log precision
prec
Precision for the lognormal observations
Log precision for the lognormal observations
0
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
The log-Normal likelihood (survival)
TRUE
FALSE
default identity
lognormal
Number of hyperparmeters is 11.
78001
log precision
prec
Precision for the lognormalsurv observations
Log precision for the lognormalsurv observations
0
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
78002
beta1
beta1
beta1 for logNormal-Cure
beta1 for logNormal-Cure
-7
FALSE
normal
-4 100
function(x) x
function(x) x
78003
beta2
beta2
beta2 for logNormal-Cure
beta2 for logNormal-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78004
beta3
beta3
beta3 for logNormal-Cure
beta3 for logNormal-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78005
beta4
beta4
beta4 for logNormal-Cure
beta4 for logNormal-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78006
beta5
beta5
beta5 for logNormal-Cure
beta5 for logNormal-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78007
beta6
beta6
beta6 for logNormal-Cure
beta6 for logNormal-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78008
beta7
beta7
beta7 for logNormal-Cure
beta7 for logNormal-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78009
beta8
beta8
beta8 for logNormal-Cure
beta8 for logNormal-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78010
beta9
beta9
beta9 for logNormal-Cure
beta9 for logNormal-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78011
beta10
beta10
beta10 for logNormal-Cure
beta10 for logNormal-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
The Exponential likelihood
FALSE
FALSE
default log
exponential
Number of hyperparmeters is 0.
The Exponential likelihood (survival)
TRUE
FALSE
default log neglog
exponential
Number of hyperparmeters is 10.
78020
beta1
beta1
beta1 for Exp-Cure
beta1 for Exp-Cure
-4
FALSE
normal
-1 100
function(x) x
function(x) x
78021
beta2
beta2
beta2 for Exp-Cure
beta2 for Exp-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78022
beta3
beta3
beta3 for Exp-Cure
beta3 for Exp-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78023
beta4
beta4
beta4 for Exp-Cure
beta4 for Exp-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78024
beta5
beta5
beta5 for Exp-Cure
beta5 for Exp-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78025
beta6
beta6
beta6 for Exp-Cure
beta6 for Exp-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78026
beta7
beta7
beta7 for Exp-Cure
beta7 for Exp-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78027
beta8
beta8
beta8 for Exp-Cure
beta8 for Exp-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78028
beta9
beta9
beta9 for Exp-Cure
beta9 for Exp-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
78029
beta10
beta10
beta10 for Exp-Cure
beta10 for Exp-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
Cox-proportional hazard likelihood
TRUE
TRUE
default log neglog
coxph
Number of hyperparmeters is 0.
The Weibull likelihood
FALSE
FALSE
default log neglog quantile
weibull
Number of hyperparmeters is 1.
79001
log alpha
alpha
alpha parameter for weibull
alpha_intern for weibull
-2
FALSE
pc.alphaw
5
function(x, sc = 0.1) log(x) / sc
function(x, sc = 0.1) exp(sc * x)
The Weibull likelihood (survival)
TRUE
FALSE
default log neglog quantile
weibull
Number of hyperparmeters is 11.
79101
log alpha
alpha
alpha parameter for weibullsurv
alpha_intern for weibullsurv
-2
FALSE
pc.alphaw
5
function(x, sc = 0.1) log(x) / sc
function(x, sc = 0.1) exp(sc * x)
79102
beta1
beta1
beta1 for Weibull-Cure
beta1 for Weibull-Cure
-7
FALSE
normal
-4 100
function(x) x
function(x) x
79103
beta2
beta2
beta2 for Weibull-Cure
beta2 for Weibull-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
79104
beta3
beta3
beta3 for Weibull-Cure
beta3 for Weibull-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
79105
beta4
beta4
beta4 for Weibull-Cure
beta4 for Weibull-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
79106
beta5
beta5
beta5 for Weibull-Cure
beta5 for Weibull-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
79107
beta6
beta6
beta6 for Weibull-Cure
beta6 for Weibull-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
79108
beta7
beta7
beta7 for Weibull-Cure
beta7 for Weibull-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
79109
beta8
beta8
beta8 for Weibull-Cure
beta8 for Weibull-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
79110
beta9
beta9
beta9 for Weibull-Cure
beta9 for Weibull-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
79111
beta10
beta10
beta10 for Weibull-Cure
beta10 for Weibull-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
The loglogistic likelihood
FALSE
FALSE
default log neglog
loglogistic
Number of hyperparmeters is 1.
80001
log alpha
alpha
alpha for loglogistic observations
log alpha for loglogistic observations
1
FALSE
loggamma
25 25
function(x) log(x)
function(x) exp(x)
The loglogistic likelihood (survival)
TRUE
FALSE
default log neglog
loglogistic
Number of hyperparmeters is 11.
80011
log alpha
alpha
alpha for loglogisticsurv observations
log alpha for loglogisticsurv observations
1
FALSE
loggamma
25 25
function(x) log(x)
function(x) exp(x)
80012
beta1
beta1
beta1 for logLogistic-Cure
beta1 for logLogistic-Cure
-5
FALSE
normal
-4 100
function(x) x
function(x) x
80013
beta2
beta2
beta2 for logLogistic-Cure
beta2 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
80014
beta3
beta3
beta3 for logLogistic-Cure
beta3 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
80015
beta4
beta4
beta4 for logLogistic-Cure
beta4 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
80016
beta5
beta5
beta5 for logLogistic-Cure
beta5 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
80017
beta6
beta6
beta6 for logLogistic-Cure
beta6 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
80018
beta7
beta7
beta7 for logLogistic-Cure
beta7 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
80019
beta8
beta8
beta8 for logLogistic-Cure
beta8 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
80020
beta9
beta9
beta9 for logLogistic-Cure
beta9 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
80021
beta10
beta10
beta10 for logLogistic-Cure
beta10 for logLogistic-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
The Gaussian stochvol likelihood
FALSE
FALSE
default log
stochvolgaussian
Number of hyperparmeters is 1.
82001
log precision
prec
Offset precision for stochvol
Log offset precision for stochvol
500
TRUE
loggamma
1 0.005
function(x) log(x)
function(x) exp(x)
The SkewNormal stochvol likelihood
experimental
FALSE
FALSE
default log
stochvolsn
Number of hyperparmeters is 2.
82101
logit skew
skew
Skewness for stochvol_sn observations
Intern skewness for stochvol_sn observations
0.00123456789
FALSE
pc.sn
10
function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))
function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)
82102
log precision
prec
Offset precision for stochvol_sn
Log offset precision for stochvol_sn
500
TRUE
loggamma
1 0.005
function(x) log(x)
function(x) exp(x)
The Student-t stochvol likelihood
FALSE
FALSE
default log
stochvolt
Number of hyperparmeters is 1.
83001
log degrees of freedom
dof
degrees of freedom for stochvol student-t
dof_intern for stochvol student-t
4
FALSE
pc.dof
15 0.5
function(x) log(x - 2)
function(x) 2 + exp(x)
The Normal inverse Gaussian stochvol likelihood
FALSE
FALSE
default log
stochvolnig
Number of hyperparmeters is 2.
84001
skewness
skew
skewness_param_intern for stochvol-nig
skewness parameter for stochvol-nig
0
FALSE
gaussian
0 10
function(x) x
function(x) x
84002
shape
shape
shape parameter for stochvol-nig
shape_param_intern for stochvol-nig
0
FALSE
loggamma
1 0.5
function(x) log(x - 1)
function(x) 1 + exp(x)
Zero-inflated Poisson, type 0
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 1.
85001
logit probability
prob
zero-probability parameter for zero-inflated poisson_0
intern zero-probability parameter for zero-inflated poisson_0
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero-inflated Poisson, type 1
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 1.
86001
logit probability
prob
zero-probability parameter for zero-inflated poisson_1
intern zero-probability parameter for zero-inflated poisson_1
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero-inflated Poisson, type 2
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 1.
87001
log alpha
a
zero-probability parameter for zero-inflated poisson_2
intern zero-probability parameter for zero-inflated poisson_2
0.693147180559945
FALSE
gaussian
0.693147180559945 1
function(x) log(x)
function(x) exp(x)
Zero-inflated censored Poisson, type 0
experimental
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 1.
87101
logit probability
prob
zero-probability parameter for zero-inflated poisson_0
intern zero-probability parameter for zero-inflated poisson_0
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero-inflated censored Poisson, type 1
experimental
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 1.
87201
logit probability
prob
zero-probability parameter for zero-inflated poisson_1
intern zero-probability parameter for zero-inflated poisson_1
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero-inflated Beta-Binomial, type 0
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
zeroinflated
Number of hyperparmeters is 2.
88001
overdispersion
rho
rho for zero-inflated betabinomial_0
rho_intern for zero-inflated betabinomial_0
0
FALSE
gaussian
0 0.4
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
88002
logit probability
prob
zero-probability parameter for zero-inflated betabinomial_0
intern zero-probability parameter for zero-inflated betabinomial_0
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero-inflated Beta-Binomial, type 1
FALSE
TRUE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
zeroinflated
Number of hyperparmeters is 2.
89001
overdispersion
rho
rho for zero-inflated betabinomial_1
rho_intern for zero-inflated betabinomial_1
0
FALSE
gaussian
0 0.4
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
89002
logit probability
prob
zero-probability parameter for zero-inflated betabinomial_1
intern zero-probability parameter for zero-inflated betabinomial_1
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero-inflated Binomial, type 0
FALSE
FALSE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
zeroinflated
Number of hyperparmeters is 1.
90001
logit probability
prob
zero-probability parameter for zero-inflated binomial_0
intern zero-probability parameter for zero-inflated binomial_0
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero-inflated Binomial, type 1
FALSE
FALSE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
zeroinflated
Number of hyperparmeters is 1.
91001
logit probability
prob
zero-probability parameter for zero-inflated binomial_1
intern zero-probability parameter for zero-inflated binomial_1
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero-inflated Binomial, type 2
FALSE
FALSE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
zeroinflated
Number of hyperparmeters is 1.
92001
alpha
alpha
zero-probability parameter for zero-inflated binomial_2
intern zero-probability parameter for zero-inflated binomial_2
-1
FALSE
gaussian
-1 0.2
function(x) log(x)
function(x) exp(x)
Zero and N inflated binomial, type 2
FALSE
FALSE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
NA
Number of hyperparmeters is 2.
93001
alpha1
alpha1
alpha1 parameter for zero-n-inflated binomial_2
intern alpha1 parameter for zero-n-inflated binomial_2
-1
FALSE
gaussian
-1 0.2
function(x) log(x)
function(x) exp(x)
93002
alpha2
alpha2
alpha2 parameter for zero-n-inflated binomial_2
intern alpha2 parameter for zero-n-inflated binomial_2
-1
FALSE
gaussian
-1 0.2
function(x) log(x)
function(x) exp(x)
Zero and N inflated binomial, type 3
experimental
FALSE
FALSE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
zeroinflated
Number of hyperparmeters is 2.
93101
alpha0
alpha0
alpha0 parameter for zero-n-inflated binomial_3
intern alpha0 parameter for zero-n-inflated binomial_3
1
FALSE
loggamma
1 1
function(x) log(x)
function(x) exp(x)
93102
alphaN
alphaN
intern alphaN parameter for zero-n-inflated binomial_3
alphaN parameter for zero-n-inflated binomial_3
1
FALSE
loggamma
1 1
function(x) log(x)
function(x) exp(x)
Zero inflated Beta-Binomial, type 2
FALSE
FALSE
default logit loga cauchit probit cloglog ccloglog loglog robit sn
zeroinflated
Number of hyperparmeters is 2.
94001
log alpha
a
zero-probability parameter for zero-inflated betabinomial_2
intern zero-probability parameter for zero-inflated betabinomial_2
0.693147180559945
FALSE
gaussian
0.693147180559945 1
function(x) log(x)
function(x) exp(x)
94002
beta
b
overdispersion parameter for zero-inflated betabinomial_2
intern overdispersion parameter for zero-inflated betabinomial_2
0
FALSE
gaussian
0 1
function(x) log(x)
function(x) exp(x)
Zero inflated negBinomial, type 0
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 2.
95001
log size
size
size for nbinomial_0 zero-inflated observations
log size for nbinomial_0 zero-inflated observations
2.30258509299405
FALSE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
95002
logit probability
prob
zero-probability parameter for zero-inflated nbinomial_0
intern zero-probability parameter for zero-inflated nbinomial_0
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero inflated negBinomial, type 1
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 2.
96001
log size
size
size for nbinomial_1 zero-inflated observations
log size for nbinomial_1 zero-inflated observations
2.30258509299405
FALSE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
96002
logit probability
prob
zero-probability parameter for zero-inflated nbinomial_1
intern zero-probability parameter for zero-inflated nbinomial_1
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero inflated negBinomial, type 1, strata 2
experimental
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 11.
97001
log size
size
size for zero-inflated nbinomial_1_strata2
log size for zero-inflated nbinomial_1_strata2
2.30258509299405
FALSE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
97002
logit probability 1
prob1
zero-probability1 for zero-inflated nbinomial_1_strata2
intern zero-probability1 for zero-inflated nbinomial_1_strata2
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
97003
logit probability 2
prob2
zero-probability2 for zero-inflated nbinomial_1_strata2
intern zero-probability2 for zero-inflated nbinomial_1_strata2
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
97004
logit probability 3
prob3
zero-probability3 for zero-inflated nbinomial_1_strata2
intern zero-probability3 for zero-inflated nbinomial_1_strata2
-1
TRUE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
97005
logit probability 4
prob4
zero-probability4 for zero-inflated nbinomial_1_strata2
intern zero-probability4 for zero-inflated nbinomial_1_strata2
-1
TRUE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
97006
logit probability 5
prob5
zero-probability5 for zero-inflated nbinomial_1_strata2
intern zero-probability5 for zero-inflated nbinomial_1_strata2
-1
TRUE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
97007
logit probability 6
prob6
zero-probability6 for zero-inflated nbinomial_1_strata2
intern zero-probability6 for zero-inflated nbinomial_1_strata2
-1
TRUE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
97008
logit probability 7
prob7
zero-probability7 for zero-inflated nbinomial_1_strata2
intern zero-probability7 for zero-inflated nbinomial_1_strata2
-1
TRUE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
97009
logit probability 8
prob8
zero-probability8 for zero-inflated nbinomial_1_strata2
intern zero-probability8 for zero-inflated nbinomial_1_strata2
-1
TRUE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
97010
logit probability 9
prob9
zero-probability9 for zero-inflated nbinomial_1_strata2
intern zero-probability9 for zero-inflated nbinomial_1_strata2
-1
TRUE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
97011
logit probability 10
prob10
zero-probability10 for zero-inflated nbinomial_1_strata2
intern zero-probability10 for zero-inflated nbinomial_1_strata2
-1
TRUE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
Zero inflated negBinomial, type 1, strata 3
experimental
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 11.
98001
logit probability
prob
zero-probability for zero-inflated nbinomial_1_strata3
intern zero-probability for zero-inflated nbinomial_1_strata3
-1
FALSE
gaussian
-1 0.2
function(x) log(x / (1 - x))
function(x) exp(x) / (1 + exp(x))
98002
log size 1
size1
size1 for zero-inflated nbinomial_1_strata3
log_size1 for zero-inflated nbinomial_1_strata3
2.30258509299405
FALSE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
98003
log size 2
size2
size2 for zero-inflated nbinomial_1_strata3
log_size2 for zero-inflated nbinomial_1_strata3
2.30258509299405
FALSE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
98004
log size 3
size3
size3 for zero-inflated nbinomial_1_strata3
log_size3 for zero-inflated nbinomial_1_strata3
2.30258509299405
TRUE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
98005
log size 4
size4
size4 for zero-inflated nbinomial_1_strata3
log_size4 for zero-inflated nbinomial_1_strata3
2.30258509299405
TRUE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
98006
log size 5
size5
size5 for zero-inflated nbinomial_1_strata3
log_size5 for zero-inflated nbinomial_1_strata3
2.30258509299405
TRUE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
98007
log size 6
size6
size6 for zero-inflated nbinomial_1_strata3
log_size6 for zero-inflated nbinomial_1_strata3
2.30258509299405
TRUE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
98008
log size 7
size7
size7 for zero-inflated nbinomial_1_strata3
log_size7 for zero-inflated nbinomial_1_strata3
2.30258509299405
TRUE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
98009
log size 8
size8
size8 for zero-inflated nbinomial_1_strata3
log_size8 for zero-inflated nbinomial_1_strata3
2.30258509299405
TRUE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
98010
log size 9
size9
size9 for zero-inflated nbinomial_1_strata3
log_size9 for zero-inflated nbinomial_1_strata3
2.30258509299405
TRUE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
98011
log size 10
size10
size10 for zero-inflated nbinomial_1_strata3
log_size10 for zero-inflated nbinomial_1_strata3
2.30258509299405
TRUE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
Zero inflated negBinomial, type 2
FALSE
FALSE
default log
zeroinflated
Number of hyperparmeters is 2.
99001
log size
size
size for nbinomial zero-inflated observations
log size for nbinomial zero-inflated observations
2.30258509299405
FALSE
pc.mgamma
7
function(x) log(x)
function(x) exp(x)
99002
log alpha
a
parameter alpha for zero-inflated nbinomial2
parameter alpha.intern for zero-inflated nbinomial2
0.693147180559945
FALSE
gaussian
2 1
function(x) log(x)
function(x) exp(x)
Student-t likelihood
FALSE
FALSE
default identity
student-t
Number of hyperparmeters is 2.
100001
log precision
prec
precision for the student-t observations
log precision for the student-t observations
0
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
100002
log degrees of freedom
dof
degrees of freedom for student-t
dof_intern for student-t
5
FALSE
pc.dof
15 0.5
function(x) log(x - 2)
function(x) 2 + exp(x)
A stratified version of the Student-t likelihood
FALSE
FALSE
default identity
tstrata
Number of hyperparmeters is 11.
101001
log degrees of freedom
dof
dof_intern for tstrata
degrees of freedom for tstrata
4
FALSE
pc.dof
15 0.5
function(x) log(x - 5)
function(x) 5 + exp(x)
101002
log precision1
prec1
Prec for tstrata strata
Log prec for tstrata strata
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
101003
log precision2
prec2
Prec for tstrata strata[2]
Log prec for tstrata strata[2]
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
101004
log precision3
prec3
Prec for tstrata strata[3]
Log prec for tstrata strata[3]
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
101005
log precision4
prec4
Prec for tstrata strata[4]
Log prec for tstrata strata[4]
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
101006
log precision5
prec5
Prec for tstrata strata[5]
Log prec for tstrata strata[5]
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
101007
log precision6
prec6
Prec for tstrata strata[6]
Log prec for tstrata strata[6]
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
101008
log precision7
prec7
Prec for tstrata strata[7]
Log prec for tstrata strata[7]
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
101009
log precision8
prec8
Prec for tstrata strata[8]
Log prec for tstrata strata[8]
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
101010
log precision9
prec9
Prec for tstrata strata[9]
Log prec for tstrata strata[9]
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
101011
log precision10
prec10
Prec for tstrata strata[10]
Log prec for tstrata strata[10]
2
FALSE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
Binomial-Poisson mixture
experimental
FALSE
TRUE
default logit loga probit
nmix
Number of hyperparmeters is 15.
101101
beta1
beta1
beta[1] for NMix observations
beta[1] for NMix observations
2.30258509299405
FALSE
normal
0 0.5
function(x) x
function(x) x
101102
beta2
beta2
beta[2] for NMix observations
beta[2] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101103
beta3
beta3
beta[3] for NMix observations
beta[3] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101104
beta4
beta4
beta[4] for NMix observations
beta[4] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101105
beta5
beta5
beta[5] for NMix observations
beta[5] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101106
beta6
beta6
beta[6] for NMix observations
beta[6] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101107
beta7
beta7
beta[7] for NMix observations
beta[7] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101108
beta8
beta8
beta[8] for NMix observations
beta[8] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101109
beta9
beta9
beta[9] for NMix observations
beta[9] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101110
beta10
beta10
beta[10] for NMix observations
beta[10] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101111
beta11
beta11
beta[11] for NMix observations
beta[11] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101112
beta12
beta12
beta[12] for NMix observations
beta[12] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101113
beta13
beta13
beta[13] for NMix observations
beta[13] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101114
beta14
beta14
beta[14] for NMix observations
beta[14] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101115
beta15
beta15
beta[15] for NMix observations
beta[15] for NMix observations
0
FALSE
normal
0 1
function(x) x
function(x) x
NegBinomial-Poisson mixture
experimental
FALSE
TRUE
default logit loga probit
nmixnb
Number of hyperparmeters is 16.
101121
beta1
beta1
beta[1] for NMixNB observations
beta[1] for NMixNB observations
2.30258509299405
FALSE
normal
0 0.5
function(x) x
function(x) x
101122
beta2
beta2
beta[2] for NMixNB observations
beta[2] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101123
beta3
beta3
beta[3] for NMixNB observations
beta[3] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101124
beta4
beta4
beta[4] for NMixNB observations
beta[4] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101125
beta5
beta5
beta[5] for NMixNB observations
beta[5] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101126
beta6
beta6
beta[6] for NMixNB observations
beta[6] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101127
beta7
beta7
beta[7] for NMixNB observations
beta[7] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101128
beta8
beta8
beta[8] for NMixNB observations
beta[8] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101129
beta9
beta9
beta[9] for NMixNB observations
beta[9] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101130
beta10
beta10
beta[10] for NMixNB observations
beta[10] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101131
beta11
beta11
beta[11] for NMixNB observations
beta[11] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101132
beta12
beta12
beta[12] for NMixNB observations
beta[12] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101133
beta13
beta13
beta[13] for NMixNB observations
beta[13] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101134
beta14
beta14
beta[14] for NMixNB observations
beta[14] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101135
beta15
beta15
beta[15] for NMixNB observations
beta[15] for NMixNB observations
0
FALSE
normal
0 1
function(x) x
function(x) x
101136
overdispersion
overdispersion
overdispersion for NMixNB observations
log_overdispersion for NMixNB observations
0
FALSE
pc.gamma
7
function(x) log(x)
function(x) exp(x)
Generalized Pareto likelihood
experimental
FALSE
TRUE
default quantile
genPareto
Number of hyperparmeters is 1.
101201
tail
xi
Tail parameter for the gp observations
Intern tail parameter for the gp observations
-4
FALSE
pc.gevtail
7 0 0.5
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))
Discrete generalized Pareto likelihood
experimental
FALSE
TRUE
default quantile
dgp
Number of hyperparmeters is 1.
101301
tail
xi
Tail parameter for the dgp observations
Intern tail parameter for the dgp observations
2
FALSE
pc.gevtail
7 0 0.5
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))
function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))
Likelihood for the log-periodogram
FALSE
FALSE
default identity
NA
Number of hyperparmeters is 0.
Tweedie distribution
FALSE
FALSE
default log
tweedie
Number of hyperparmeters is 2.
102101
p
p
p parameter for Tweedie
p_intern parameter for Tweedie
0
FALSE
normal
0 100
function(x, interval = c(1.0, 2.0)) log(-(interval[1] - x) / (interval[2] - x))
function(x, interval = c(1.0, 2.0)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))
102201
dispersion
phi
Dispersion parameter for Tweedie
Log dispersion parameter for Tweedie
-4
FALSE
loggamma
100 100
function(x) log(x)
function(x) exp(x)
fmri distribution (special nc-chi)
experimental
FALSE
FALSE
default log
fmri
Number of hyperparmeters is 2.
103101
precision
prec
Precision for fmri
Log precision for fmri
0
FALSE
loggamma
10 10
function(x) log(x)
function(x) exp(x)
103202
dof
df
NOT IN USE
NOT IN USE
4
TRUE
normal
0 1
function(x) x
function(x) x
fmri distribution (special nc-chi)
experimental
TRUE
FALSE
default log
fmri
Number of hyperparmeters is 2.
104101
precision
prec
Precision for fmrisurv
Log precision for fmrisurv
0
FALSE
loggamma
10 10
function(x) log(x)
function(x) exp(x)
104201
dof
df
NOT IN USE
NOT IN USE
4
TRUE
normal
0 1
function(x) x
function(x) x
gompertz distribution
experimental
FALSE
FALSE
default log neglog
gompertz
Number of hyperparmeters is 1.
105101
shape
alpha
alpha_intern for Gompertz
alpha parameter for Gompertz
-1
FALSE
normal
0 1
function(x, sc = 0.1) log(x) / sc
function(x, sc = 0.1) exp(sc * x)
gompertz distribution
experimental
TRUE
FALSE
default log neglog
gompertz
Number of hyperparmeters is 11.
106101
shape
alpha
alpha_intern for Gompertz-surv
alpha parameter for Gompertz-surv
-10
FALSE
normal
0 1
function(x, sc = 0.1) log(x) / sc
function(x, sc = 0.1) exp(sc * x)
106102
beta1
beta1
beta1 for Gompertz-Cure
beta1 for Gompertz-Cure
-5
FALSE
normal
-4 100
function(x) x
function(x) x
106103
beta2
beta2
beta2 for Gompertz-Cure
beta2 for Gompertz-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
106104
beta3
beta3
beta3 for Gompertz-Cure
beta3 for Gompertz-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
106105
beta4
beta4
beta4 for Gompertz-Cure
beta4 for Gompertz-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
106106
beta5
beta5
beta5 for Gompertz-Cure
beta5 for Gompertz-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
106107
beta6
beta6
beta6 for Gompertz-Cure
beta6 for Gompertz-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
106108
beta7
beta7
beta7 for Gompertz-Cure
beta7 for Gompertz-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
106109
beta8
beta8
beta8 for Gompertz-Cure
beta8 for Gompertz-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
106110
beta9
beta9
beta9 for Gompertz-Cure
beta9 for Gompertz-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
106111
beta10
beta10
beta10 for Gompertz-Cure
beta10 for Gompertz-Cure
0
FALSE
normal
0 100
function(x) x
function(x) x
Valid models in this section are:
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 4
Number of parameters in the prior = 7
Number of parameters in the prior = 11
Number of parameters in the prior = 16
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 0
Number of parameters in the prior = 0
Number of parameters in the prior = 0
Number of parameters in the prior = -1
Number of parameters in the prior = 1
Number of parameters in the prior = 1
Number of parameters in the prior = 1
Number of parameters in the prior = 0
Number of parameters in the prior = 0
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 2
Number of parameters in the prior = 4
Number of parameters in the prior = 3
Number of parameters in the prior = 2
Number of parameters in the prior = 1
Number of parameters in the prior = 1
Number of parameters in the prior = 1
Number of parameters in the prior = 1
Number of parameters in the prior = 3
Number of parameters in the prior = 2
Number of parameters in the prior = 0
Number of parameters in the prior = 0
Number of parameters in the prior = 0
Number of parameters in the prior = 211
Number of parameters in the prior = -1
Number of parameters in the prior = -1
Number of parameters in the prior = 0
Valid models in this section are:
(experimental)
FALSE
FALSE
FALSE
1
NULL
NULL
FALSE
FALSE
NA
Number of hyperparmeters is 1.
102001
log precision
prec
NOT IN USE
NOT IN USE
0
TRUE
loggamma
1 5e-05
function(x) log(x)
function(x) exp(x)
Valid models in this section are:
lp.scale
Number of hyperparmeters is 100.
103001
beta1
b1
beta[1] for lp_scale
beta[1] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103002
beta2
b2
beta[2] for lp_scale
beta[2] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103003
beta3
b3
beta[3] for lp_scale
beta[3] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103004
beta4
b4
beta[4] for lp_scale
beta[4] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103005
beta5
b5
beta[5] for lp_scale
beta[5] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103006
beta6
b6
beta[6] for lp_scale
beta[6] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103007
beta7
b7
beta[7] for lp_scale
beta[7] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103008
beta8
b8
beta[8] for lp_scale
beta[8] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103009
beta9
b9
beta[9] for lp_scale
beta[9] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103010
beta10
b10
beta[10] for lp_scale
beta[10] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103011
beta11
b11
beta[11] for lp_scale
beta[11] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103012
beta12
b12
beta[12] for lp_scale
beta[12] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103013
beta13
b13
beta[13] for lp_scale
beta[13] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103014
beta14
b14
beta[14] for lp_scale
beta[14] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103015
beta15
b15
beta[15] for lp_scale
beta[15] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103016
beta16
b16
beta[16] for lp_scale
beta[16] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103017
beta17
b17
beta[17] for lp_scale
beta[17] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103018
beta18
b18
beta[18] for lp_scale
beta[18] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103019
beta19
b19
beta[19] for lp_scale
beta[19] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103020
beta20
b20
beta[20] for lp_scale
beta[20] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103021
beta21
b21
beta[21] for lp_scale
beta[21] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103022
beta22
b22
beta[22] for lp_scale
beta[22] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103023
beta23
b23
beta[23] for lp_scale
beta[23] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103024
beta24
b24
beta[24] for lp_scale
beta[24] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103025
beta25
b25
beta[25] for lp_scale
beta[25] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103026
beta26
b26
beta[26] for lp_scale
beta[26] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103027
beta27
b27
beta[27] for lp_scale
beta[27] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103028
beta28
b28
beta[28] for lp_scale
beta[28] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103029
beta29
b29
beta[29] for lp_scale
beta[29] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103030
beta30
b30
beta[30] for lp_scale
beta[30] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103031
beta31
b31
beta[31] for lp_scale
beta[31] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103032
beta32
b32
beta[32] for lp_scale
beta[32] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103033
beta33
b33
beta[33] for lp_scale
beta[33] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103034
beta34
b34
beta[34] for lp_scale
beta[34] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103035
beta35
b35
beta[35] for lp_scale
beta[35] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103036
beta36
b36
beta[36] for lp_scale
beta[36] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103037
beta37
b37
beta[37] for lp_scale
beta[37] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103038
beta38
b38
beta[38] for lp_scale
beta[38] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103039
beta39
b39
beta[39] for lp_scale
beta[39] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103040
beta40
b40
beta[40] for lp_scale
beta[40] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103041
beta41
b41
beta[41] for lp_scale
beta[41] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103042
beta42
b42
beta[42] for lp_scale
beta[42] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103043
beta43
b43
beta[43] for lp_scale
beta[43] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103044
beta44
b44
beta[44] for lp_scale
beta[44] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103045
beta45
b45
beta[45] for lp_scale
beta[45] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103046
beta46
b46
beta[46] for lp_scale
beta[46] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103047
beta47
b47
beta[47] for lp_scale
beta[47] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103048
beta48
b48
beta[48] for lp_scale
beta[48] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103049
beta49
b49
beta[49] for lp_scale
beta[49] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103050
beta50
b50
beta[50] for lp_scale
beta[50] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103051
beta51
b51
beta[51] for lp_scale
beta[51] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103052
beta52
b52
beta[52] for lp_scale
beta[52] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103053
beta53
b53
beta[53] for lp_scale
beta[53] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103054
beta54
b54
beta[54] for lp_scale
beta[54] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103055
beta55
b55
beta[55] for lp_scale
beta[55] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103056
beta56
b56
beta[56] for lp_scale
beta[56] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103057
beta57
b57
beta[57] for lp_scale
beta[57] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103058
beta58
b58
beta[58] for lp_scale
beta[58] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103059
beta59
b59
beta[59] for lp_scale
beta[59] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103060
beta60
b60
beta[60] for lp_scale
beta[60] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103061
beta61
b61
beta[61] for lp_scale
beta[61] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103062
beta62
b62
beta[62] for lp_scale
beta[62] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103063
beta63
b63
beta[63] for lp_scale
beta[63] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103064
beta64
b64
beta[64] for lp_scale
beta[64] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103065
beta65
b65
beta[65] for lp_scale
beta[65] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103066
beta66
b66
beta[66] for lp_scale
beta[66] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103067
beta67
b67
beta[67] for lp_scale
beta[67] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103068
beta68
b68
beta[68] for lp_scale
beta[68] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103069
beta69
b69
beta[69] for lp_scale
beta[69] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103070
beta70
b70
beta[70] for lp_scale
beta[70] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103071
beta71
b71
beta[71] for lp_scale
beta[71] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103072
beta72
b72
beta[72] for lp_scale
beta[72] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103073
beta73
b73
beta[73] for lp_scale
beta[73] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103074
beta74
b74
beta[74] for lp_scale
beta[74] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103075
beta75
b75
beta[75] for lp_scale
beta[75] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103076
beta76
b76
beta[76] for lp_scale
beta[76] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103077
beta77
b77
beta[77] for lp_scale
beta[77] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103078
beta78
b78
beta[78] for lp_scale
beta[78] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103079
beta79
b79
beta[79] for lp_scale
beta[79] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103080
beta80
b80
beta[80] for lp_scale
beta[80] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103081
beta81
b81
beta[81] for lp_scale
beta[81] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103082
beta82
b82
beta[82] for lp_scale
beta[82] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103083
beta83
b83
beta[83] for lp_scale
beta[83] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103084
beta84
b84
beta[84] for lp_scale
beta[84] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103085
beta85
b85
beta[85] for lp_scale
beta[85] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103086
beta86
b86
beta[86] for lp_scale
beta[86] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103087
beta87
b87
beta[87] for lp_scale
beta[87] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103088
beta88
b88
beta[88] for lp_scale
beta[88] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103089
beta89
b89
beta[89] for lp_scale
beta[89] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103090
beta90
b90
beta[90] for lp_scale
beta[90] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103091
beta91
b91
beta[91] for lp_scale
beta[91] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103092
beta92
b92
beta[92] for lp_scale
beta[92] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103093
beta93
b93
beta[93] for lp_scale
beta[93] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103094
beta94
b94
beta[94] for lp_scale
beta[94] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103095
beta95
b95
beta[95] for lp_scale
beta[95] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103096
beta96
b96
beta[96] for lp_scale
beta[96] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103097
beta97
b97
beta[97] for lp_scale
beta[97] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103098
beta98
b98
beta[98] for lp_scale
beta[98] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103099
beta99
b99
beta[99] for lp_scale
beta[99] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
103100
beta100
b100
beta[100] for lp_scale
beta[100] for lp_scale
1
FALSE
normal
1 10
function(x) x
function(x) x
## 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)))