inla.models {INLA} | R Documentation

## Valid models in INLA

### Description

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

### Usage

    inla.models()

### Value

Valid sections are: latent, group, mix, link, predictor, hazard, likelihood, prior, wrapper

Section ‘latent’.

  Valid models in this section are:

  Model ‘linear’.

    Number of hyperparmeters are 0.

  Model ‘iid’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘1001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Gaussian random effects in dim=1’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘indep’

  Model ‘mec’.

    Number of hyperparmeters are 4.

    Hyperparameter ‘theta1’
      hyperid =

        ‘2001’

      name =

        ‘beta’

      short.name =

        ‘b’

      prior =

        ‘gaussian’

      param =

        ‘1 0.001’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘2002’

      name =

        ‘prec.u’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 1e-04’

      initial =

        ‘9.21034037197618’

      fixed =

        ‘TRUE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘2003’

      name =

        ‘mean.x’

      short.name =

        ‘mu.x’

      prior =

        ‘gaussian’

      param =

        ‘0 1e-04’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘2004’

      name =

        ‘prec.x’

      short.name =

        ‘prec.x’

      prior =

        ‘loggamma’

      param =

        ‘1 10000’

      initial =

        ‘-9.21034037197618’

      fixed =

        ‘TRUE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Classical measurement error model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘mec’

  Model ‘meb’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘3001’

      name =

        ‘beta’

      short.name =

        ‘b’

      prior =

        ‘gaussian’

      param =

        ‘1 0.001’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘3002’

      name =

        ‘prec.u’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 1e-04’

      initial =

        ‘6.90775527898214’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Berkson measurement error model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘meb’

  Model ‘rgeneric’.

    Number of hyperparmeters are 0.

  Model ‘rw1’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘4001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Random walk of order 1’

      constr =

        ‘TRUE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      min.diff =

        ‘1e-05’

      pdf =

        ‘rw1’

  Model ‘rw2’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘5001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Random walk of order 2’

      constr =

        ‘TRUE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      min.diff =

        ‘0.001’

      pdf =

        ‘rw2’

  Model ‘crw2’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘6001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Exact solution to the random walk of order 2’

      constr =

        ‘TRUE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘2’

      aug.constr =

        ‘1’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      min.diff =

        ‘0.001’

      pdf =

        ‘crw2’

  Model ‘seasonal’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘7001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Seasonal model for time series’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘seasonal’

  Model ‘besag’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘8001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Besag area model (CAR-model)’

      constr =

        ‘TRUE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘besag’

  Model ‘besag2’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘9001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘9002’

      name =

        ‘scaling parameter’

      short.name =

        ‘a’

      prior =

        ‘loggamma’

      param =

        ‘10 10’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The shared Besag model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘1 2’

      n.div.by =

        ‘2’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘besag2’

  Model ‘bym’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘10001’

      name =

        ‘log unstructured precision’

      short.name =

        ‘prec.unstruct’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-04’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘10002’

      name =

        ‘log spatial precision’

      short.name =

        ‘prec.spatial’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-04’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The BYM-model (Besag-York-Mollier model)’

      constr =

        ‘TRUE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘TRUE’

      aug.factor =

        ‘2’

      aug.constr =

        ‘2’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘bym’

  Model ‘bym2’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘11001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘pc.prec’

      param =

        ‘1 0.01’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘11002’

      name =

        ‘logit phi’

      short.name =

        ‘phi’

      prior =

        ‘pc’

      param =

        ‘0.5 0.5’

      initial =

        ‘-3’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘The BYM-model with the PC priors’

      constr =

        ‘TRUE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘TRUE’

      aug.factor =

        ‘2’

      aug.constr =

        ‘2’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      status =

        ‘experimental’

      pdf =

        ‘bym2’

  Model ‘besagproper’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘12001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-04’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘12002’

      name =

        ‘log diagonal’

      short.name =

        ‘diag’

      prior =

        ‘loggamma’

      param =

        ‘1 1’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A proper version of the Besag model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      status =

        ‘experimental’

      pdf =

        ‘besagproper’

  Model ‘besagproper2’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘13001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-04’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘13002’

      name =

        ‘logit lambda’

      short.name =

        ‘lambda’

      prior =

        ‘gaussian’

      param =

        ‘0 0.45’

      initial =

        ‘3’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘An alternative proper version of the Besag model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      status =

        ‘experimental’

      pdf =

        ‘besagproper2’

  Model ‘fgn’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘13101’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘pc.prec’

      param =

        ‘3 0.01’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘13102’

      name =

        ‘logit H’

      short.name =

        ‘H’

      prior =

        ‘pcfgnh’

      param =

        ‘0.9 0.1’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Fractional Gaussian noise model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘TRUE’

      aug.factor =

        ‘5’

      aug.constr =

        ‘1’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘TRUE’

      order.default =

        ‘4’

      order.defined =

        ‘3 4’

      pdf =

        ‘fgn’

  Model ‘fgn2’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘13101’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘pc.prec’

      param =

        ‘3 0.01’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘13102’

      name =

        ‘logit H’

      short.name =

        ‘H’

      prior =

        ‘pcfgnh’

      param =

        ‘0.9 0.1’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Fractional Gaussian noise model (alt 2)’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘TRUE’

      aug.factor =

        ‘4’

      aug.constr =

        ‘1’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘TRUE’

      order.default =

        ‘4’

      order.defined =

        ‘3 4’

      pdf =

        ‘fgn’

  Model ‘ar1’.

    Number of hyperparmeters are 3.

    Hyperparameter ‘theta1’
      hyperid =

        ‘14001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘14002’

      name =

        ‘logit lag one correlation’

      short.name =

        ‘rho’

      prior =

        ‘normal’

      param =

        ‘0 0.15’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta3’
      hyperid =

        ‘14003’

      name =

        ‘mean’

      short.name =

        ‘mean’

      prior =

        ‘normal’

      param =

        ‘0 1’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘Auto-regressive model of order 1 (AR(1))’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘ar1’

  Model ‘ar1c’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘14101’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘pc.prec’

      param =

        ‘1 0.01’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘14102’

      name =

        ‘logit lag one correlation’

      short.name =

        ‘rho’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.5’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Auto-regressive model of order 1 w/covariates’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘TRUE’

      status =

        ‘experimental’

      pdf =

        ‘ar1c’

  Model ‘ar’.

    Number of hyperparmeters are 11.

    Hyperparameter ‘theta1’
      hyperid =

        ‘15001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.prec’

      param =

        ‘3 0.01’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘15002’

      name =

        ‘pacf1’

      short.name =

        ‘pacf1’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.5’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta3’
      hyperid =

        ‘15003’

      name =

        ‘pacf2’

      short.name =

        ‘pacf2’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.4’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta4’
      hyperid =

        ‘15004’

      name =

        ‘pacf3’

      short.name =

        ‘pacf3’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.3’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta5’
      hyperid =

        ‘15005’

      name =

        ‘pacf4’

      short.name =

        ‘pacf4’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta6’
      hyperid =

        ‘15006’

      name =

        ‘pacf5’

      short.name =

        ‘pacf5’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta7’
      hyperid =

        ‘15007’

      name =

        ‘pacf6’

      short.name =

        ‘pacf6’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta8’
      hyperid =

        ‘15008’

      name =

        ‘pacf7’

      short.name =

        ‘pacf7’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta9’
      hyperid =

        ‘15009’

      name =

        ‘pacf8’

      short.name =

        ‘pacf8’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta10’
      hyperid =

        ‘15010’

      name =

        ‘pacf9’

      short.name =

        ‘pacf9’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta11’
      hyperid =

        ‘15011’

      name =

        ‘pacf10’

      short.name =

        ‘pacf10’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Auto-regressive model of order p (AR(p))’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘ar’

  Model ‘ou’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘16001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘16002’

      name =

        ‘log phi’

      short.name =

        ‘phi’

      prior =

        ‘normal’

      param =

        ‘0 0.2’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Ornstein-Uhlenbeck process’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘ou’

  Model ‘generic’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘17001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A generic model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘generic0’

  Model ‘generic0’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘18001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A generic model (type 0)’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘generic0’

  Model ‘generic1’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘19001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘19002’

      name =

        ‘beta’

      short.name =

        ‘beta’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 0.1’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘A generic model (type 1)’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘generic1’

  Model ‘generic2’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘20001’

      name =

        ‘log precision cmatrix’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘20002’

      name =

        ‘log precision random’

      short.name =

        ‘prec.random’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 0.001’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A generic model (type 2)’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘2’

      aug.constr =

        ‘2’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘generic2’

  Model ‘generic3’.

    Number of hyperparmeters are 11.

    Hyperparameter ‘theta1’
      hyperid =

        ‘21001’

      name =

        ‘log precision1’

      short.name =

        ‘prec1’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘21002’

      name =

        ‘log precision2’

      short.name =

        ‘prec2’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘21003’

      name =

        ‘log precision3’

      short.name =

        ‘prec3’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘21004’

      name =

        ‘log precision4’

      short.name =

        ‘prec4’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘21005’

      name =

        ‘log precision5’

      short.name =

        ‘prec5’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘21006’

      name =

        ‘log precision6’

      short.name =

        ‘prec6’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta7’
      hyperid =

        ‘21007’

      name =

        ‘log precision7’

      short.name =

        ‘prec7’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta8’
      hyperid =

        ‘21008’

      name =

        ‘log precision8’

      short.name =

        ‘prec8’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta9’
      hyperid =

        ‘21009’

      name =

        ‘log precision9’

      short.name =

        ‘prec9’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta10’
      hyperid =

        ‘21010’

      name =

        ‘log precision10’

      short.name =

        ‘prec10’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta11’
      hyperid =

        ‘21011’

      name =

        ‘log precision common’

      short.name =

        ‘prec.common’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A generic model (type 3)’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      status =

        ‘experimental’

      pdf =

        ‘generic3’

  Model ‘spde’.

    Number of hyperparmeters are 4.

    Hyperparameter ‘theta1’
      hyperid =

        ‘22001’

      name =

        ‘theta.T’

      short.name =

        ‘T’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘22002’

      name =

        ‘theta.K’

      short.name =

        ‘K’

      initial =

        ‘-2’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘22003’

      name =

        ‘theta.KT’

      short.name =

        ‘KT’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘22004’

      name =

        ‘theta.OC’

      short.name =

        ‘OC’

      initial =

        ‘-20’

      fixed =

        ‘TRUE’

      prior =

        ‘normal’

      param =

        ‘0 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘A SPDE model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘spde’

  Model ‘spde2’.

    Number of hyperparmeters are 100.

    Hyperparameter ‘theta1’
      hyperid =

        ‘23001’

      name =

        ‘theta1’

      short.name =

        ‘t1’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘mvnorm’

      param =

        ‘1 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘23002’

      name =

        ‘theta2’

      short.name =

        ‘t2’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘23003’

      name =

        ‘theta3’

      short.name =

        ‘t3’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘23004’

      name =

        ‘theta4’

      short.name =

        ‘t4’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘23005’

      name =

        ‘theta5’

      short.name =

        ‘t5’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘23006’

      name =

        ‘theta6’

      short.name =

        ‘t6’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta7’
      hyperid =

        ‘23007’

      name =

        ‘theta7’

      short.name =

        ‘t7’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta8’
      hyperid =

        ‘23008’

      name =

        ‘theta8’

      short.name =

        ‘t8’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta9’
      hyperid =

        ‘23009’

      name =

        ‘theta9’

      short.name =

        ‘t9’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta10’
      hyperid =

        ‘23010’

      name =

        ‘theta10’

      short.name =

        ‘t10’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta11’
      hyperid =

        ‘23011’

      name =

        ‘theta11’

      short.name =

        ‘t11’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta12’
      hyperid =

        ‘23012’

      name =

        ‘theta12’

      short.name =

        ‘t12’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta13’
      hyperid =

        ‘23013’

      name =

        ‘theta13’

      short.name =

        ‘t13’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta14’
      hyperid =

        ‘23014’

      name =

        ‘theta14’

      short.name =

        ‘t14’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta15’
      hyperid =

        ‘23015’

      name =

        ‘theta15’

      short.name =

        ‘t15’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta16’
      hyperid =

        ‘23016’

      name =

        ‘theta16’

      short.name =

        ‘t16’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta17’
      hyperid =

        ‘23017’

      name =

        ‘theta17’

      short.name =

        ‘t17’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta18’
      hyperid =

        ‘23018’

      name =

        ‘theta18’

      short.name =

        ‘t18’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta19’
      hyperid =

        ‘23019’

      name =

        ‘theta19’

      short.name =

        ‘t19’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta20’
      hyperid =

        ‘23020’

      name =

        ‘theta20’

      short.name =

        ‘t20’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta21’
      hyperid =

        ‘23021’

      name =

        ‘theta21’

      short.name =

        ‘t21’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta22’
      hyperid =

        ‘23022’

      name =

        ‘theta22’

      short.name =

        ‘t22’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta23’
      hyperid =

        ‘23023’

      name =

        ‘theta23’

      short.name =

        ‘t23’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta24’
      hyperid =

        ‘23024’

      name =

        ‘theta24’

      short.name =

        ‘t24’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta25’
      hyperid =

        ‘23025’

      name =

        ‘theta25’

      short.name =

        ‘t25’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta26’
      hyperid =

        ‘23026’

      name =

        ‘theta26’

      short.name =

        ‘t26’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta27’
      hyperid =

        ‘23027’

      name =

        ‘theta27’

      short.name =

        ‘t27’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta28’
      hyperid =

        ‘23028’

      name =

        ‘theta28’

      short.name =

        ‘t28’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta29’
      hyperid =

        ‘23029’

      name =

        ‘theta29’

      short.name =

        ‘t29’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta30’
      hyperid =

        ‘23030’

      name =

        ‘theta30’

      short.name =

        ‘t30’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta31’
      hyperid =

        ‘23031’

      name =

        ‘theta31’

      short.name =

        ‘t31’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta32’
      hyperid =

        ‘23032’

      name =

        ‘theta32’

      short.name =

        ‘t32’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta33’
      hyperid =

        ‘23033’

      name =

        ‘theta33’

      short.name =

        ‘t33’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta34’
      hyperid =

        ‘23034’

      name =

        ‘theta34’

      short.name =

        ‘t34’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta35’
      hyperid =

        ‘23035’

      name =

        ‘theta35’

      short.name =

        ‘t35’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta36’
      hyperid =

        ‘23036’

      name =

        ‘theta36’

      short.name =

        ‘t36’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta37’
      hyperid =

        ‘23037’

      name =

        ‘theta37’

      short.name =

        ‘t37’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta38’
      hyperid =

        ‘23038’

      name =

        ‘theta38’

      short.name =

        ‘t38’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta39’
      hyperid =

        ‘23039’

      name =

        ‘theta39’

      short.name =

        ‘t39’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta40’
      hyperid =

        ‘23040’

      name =

        ‘theta40’

      short.name =

        ‘t40’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta41’
      hyperid =

        ‘23041’

      name =

        ‘theta41’

      short.name =

        ‘t41’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta42’
      hyperid =

        ‘23042’

      name =

        ‘theta42’

      short.name =

        ‘t42’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta43’
      hyperid =

        ‘23043’

      name =

        ‘theta43’

      short.name =

        ‘t43’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta44’
      hyperid =

        ‘23044’

      name =

        ‘theta44’

      short.name =

        ‘t44’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta45’
      hyperid =

        ‘23045’

      name =

        ‘theta45’

      short.name =

        ‘t45’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta46’
      hyperid =

        ‘23046’

      name =

        ‘theta46’

      short.name =

        ‘t46’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta47’
      hyperid =

        ‘23047’

      name =

        ‘theta47’

      short.name =

        ‘t47’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta48’
      hyperid =

        ‘23048’

      name =

        ‘theta48’

      short.name =

        ‘t48’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta49’
      hyperid =

        ‘23049’

      name =

        ‘theta49’

      short.name =

        ‘t49’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta50’
      hyperid =

        ‘23050’

      name =

        ‘theta50’

      short.name =

        ‘t50’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta51’
      hyperid =

        ‘23051’

      name =

        ‘theta51’

      short.name =

        ‘t51’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta52’
      hyperid =

        ‘23052’

      name =

        ‘theta52’

      short.name =

        ‘t52’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta53’
      hyperid =

        ‘23053’

      name =

        ‘theta53’

      short.name =

        ‘t53’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta54’
      hyperid =

        ‘23054’

      name =

        ‘theta54’

      short.name =

        ‘t54’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta55’
      hyperid =

        ‘23055’

      name =

        ‘theta55’

      short.name =

        ‘t55’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta56’
      hyperid =

        ‘23056’

      name =

        ‘theta56’

      short.name =

        ‘t56’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta57’
      hyperid =

        ‘23057’

      name =

        ‘theta57’

      short.name =

        ‘t57’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta58’
      hyperid =

        ‘23058’

      name =

        ‘theta58’

      short.name =

        ‘t58’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta59’
      hyperid =

        ‘23059’

      name =

        ‘theta59’

      short.name =

        ‘t59’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta60’
      hyperid =

        ‘23060’

      name =

        ‘theta60’

      short.name =

        ‘t60’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta61’
      hyperid =

        ‘23061’

      name =

        ‘theta61’

      short.name =

        ‘t61’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta62’
      hyperid =

        ‘23062’

      name =

        ‘theta62’

      short.name =

        ‘t62’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta63’
      hyperid =

        ‘23063’

      name =

        ‘theta63’

      short.name =

        ‘t63’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta64’
      hyperid =

        ‘23064’

      name =

        ‘theta64’

      short.name =

        ‘t64’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta65’
      hyperid =

        ‘23065’

      name =

        ‘theta65’

      short.name =

        ‘t65’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta66’
      hyperid =

        ‘23066’

      name =

        ‘theta66’

      short.name =

        ‘t66’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta67’
      hyperid =

        ‘23067’

      name =

        ‘theta67’

      short.name =

        ‘t67’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta68’
      hyperid =

        ‘23068’

      name =

        ‘theta68’

      short.name =

        ‘t68’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta69’
      hyperid =

        ‘23069’

      name =

        ‘theta69’

      short.name =

        ‘t69’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta70’
      hyperid =

        ‘23070’

      name =

        ‘theta70’

      short.name =

        ‘t70’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta71’
      hyperid =

        ‘23071’

      name =

        ‘theta71’

      short.name =

        ‘t71’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta72’
      hyperid =

        ‘23072’

      name =

        ‘theta72’

      short.name =

        ‘t72’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta73’
      hyperid =

        ‘23073’

      name =

        ‘theta73’

      short.name =

        ‘t73’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta74’
      hyperid =

        ‘23074’

      name =

        ‘theta74’

      short.name =

        ‘t74’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta75’
      hyperid =

        ‘23075’

      name =

        ‘theta75’

      short.name =

        ‘t75’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta76’
      hyperid =

        ‘23076’

      name =

        ‘theta76’

      short.name =

        ‘t76’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta77’
      hyperid =

        ‘23077’

      name =

        ‘theta77’

      short.name =

        ‘t77’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta78’
      hyperid =

        ‘23078’

      name =

        ‘theta78’

      short.name =

        ‘t78’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta79’
      hyperid =

        ‘23079’

      name =

        ‘theta79’

      short.name =

        ‘t79’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta80’
      hyperid =

        ‘23080’

      name =

        ‘theta80’

      short.name =

        ‘t80’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta81’
      hyperid =

        ‘23081’

      name =

        ‘theta81’

      short.name =

        ‘t81’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta82’
      hyperid =

        ‘23082’

      name =

        ‘theta82’

      short.name =

        ‘t82’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta83’
      hyperid =

        ‘23083’

      name =

        ‘theta83’

      short.name =

        ‘t83’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta84’
      hyperid =

        ‘23084’

      name =

        ‘theta84’

      short.name =

        ‘t84’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta85’
      hyperid =

        ‘23085’

      name =

        ‘theta85’

      short.name =

        ‘t85’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta86’
      hyperid =

        ‘23086’

      name =

        ‘theta86’

      short.name =

        ‘t86’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta87’
      hyperid =

        ‘23087’

      name =

        ‘theta87’

      short.name =

        ‘t87’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta88’
      hyperid =

        ‘23088’

      name =

        ‘theta88’

      short.name =

        ‘t88’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta89’
      hyperid =

        ‘23089’

      name =

        ‘theta89’

      short.name =

        ‘t89’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta90’
      hyperid =

        ‘23090’

      name =

        ‘theta90’

      short.name =

        ‘t90’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta91’
      hyperid =

        ‘23091’

      name =

        ‘theta91’

      short.name =

        ‘t91’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta92’
      hyperid =

        ‘23092’

      name =

        ‘theta92’

      short.name =

        ‘t92’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta93’
      hyperid =

        ‘23093’

      name =

        ‘theta93’

      short.name =

        ‘t93’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta94’
      hyperid =

        ‘23094’

      name =

        ‘theta94’

      short.name =

        ‘t94’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta95’
      hyperid =

        ‘23095’

      name =

        ‘theta95’

      short.name =

        ‘t95’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta96’
      hyperid =

        ‘23096’

      name =

        ‘theta96’

      short.name =

        ‘t96’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta97’
      hyperid =

        ‘23097’

      name =

        ‘theta97’

      short.name =

        ‘t97’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta98’
      hyperid =

        ‘23098’

      name =

        ‘theta98’

      short.name =

        ‘t98’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta99’
      hyperid =

        ‘23099’

      name =

        ‘theta99’

      short.name =

        ‘t99’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta100’
      hyperid =

        ‘23100’

      name =

        ‘theta100’

      short.name =

        ‘t100’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘A SPDE2 model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘spde2’

  Model ‘spde3’.

    Number of hyperparmeters are 100.

    Hyperparameter ‘theta1’
      hyperid =

        ‘24001’

      name =

        ‘theta1’

      short.name =

        ‘t1’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘mvnorm’

      param =

        ‘1 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘24002’

      name =

        ‘theta2’

      short.name =

        ‘t2’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘24003’

      name =

        ‘theta3’

      short.name =

        ‘t3’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘24004’

      name =

        ‘theta4’

      short.name =

        ‘t4’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘24005’

      name =

        ‘theta5’

      short.name =

        ‘t5’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘24006’

      name =

        ‘theta6’

      short.name =

        ‘t6’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta7’
      hyperid =

        ‘24007’

      name =

        ‘theta7’

      short.name =

        ‘t7’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta8’
      hyperid =

        ‘24008’

      name =

        ‘theta8’

      short.name =

        ‘t8’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta9’
      hyperid =

        ‘24009’

      name =

        ‘theta9’

      short.name =

        ‘t9’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta10’
      hyperid =

        ‘24010’

      name =

        ‘theta10’

      short.name =

        ‘t10’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta11’
      hyperid =

        ‘24011’

      name =

        ‘theta11’

      short.name =

        ‘t11’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta12’
      hyperid =

        ‘24012’

      name =

        ‘theta12’

      short.name =

        ‘t12’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta13’
      hyperid =

        ‘24013’

      name =

        ‘theta13’

      short.name =

        ‘t13’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta14’
      hyperid =

        ‘24014’

      name =

        ‘theta14’

      short.name =

        ‘t14’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta15’
      hyperid =

        ‘24015’

      name =

        ‘theta15’

      short.name =

        ‘t15’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta16’
      hyperid =

        ‘24016’

      name =

        ‘theta16’

      short.name =

        ‘t16’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta17’
      hyperid =

        ‘24017’

      name =

        ‘theta17’

      short.name =

        ‘t17’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta18’
      hyperid =

        ‘24018’

      name =

        ‘theta18’

      short.name =

        ‘t18’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta19’
      hyperid =

        ‘24019’

      name =

        ‘theta19’

      short.name =

        ‘t19’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta20’
      hyperid =

        ‘24020’

      name =

        ‘theta20’

      short.name =

        ‘t20’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta21’
      hyperid =

        ‘24021’

      name =

        ‘theta21’

      short.name =

        ‘t21’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta22’
      hyperid =

        ‘24022’

      name =

        ‘theta22’

      short.name =

        ‘t22’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta23’
      hyperid =

        ‘24023’

      name =

        ‘theta23’

      short.name =

        ‘t23’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta24’
      hyperid =

        ‘24024’

      name =

        ‘theta24’

      short.name =

        ‘t24’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta25’
      hyperid =

        ‘24025’

      name =

        ‘theta25’

      short.name =

        ‘t25’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta26’
      hyperid =

        ‘24026’

      name =

        ‘theta26’

      short.name =

        ‘t26’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta27’
      hyperid =

        ‘24027’

      name =

        ‘theta27’

      short.name =

        ‘t27’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta28’
      hyperid =

        ‘24028’

      name =

        ‘theta28’

      short.name =

        ‘t28’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta29’
      hyperid =

        ‘24029’

      name =

        ‘theta29’

      short.name =

        ‘t29’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta30’
      hyperid =

        ‘24030’

      name =

        ‘theta30’

      short.name =

        ‘t30’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta31’
      hyperid =

        ‘24031’

      name =

        ‘theta31’

      short.name =

        ‘t31’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta32’
      hyperid =

        ‘24032’

      name =

        ‘theta32’

      short.name =

        ‘t32’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta33’
      hyperid =

        ‘24033’

      name =

        ‘theta33’

      short.name =

        ‘t33’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta34’
      hyperid =

        ‘24034’

      name =

        ‘theta34’

      short.name =

        ‘t34’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta35’
      hyperid =

        ‘24035’

      name =

        ‘theta35’

      short.name =

        ‘t35’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta36’
      hyperid =

        ‘24036’

      name =

        ‘theta36’

      short.name =

        ‘t36’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta37’
      hyperid =

        ‘24037’

      name =

        ‘theta37’

      short.name =

        ‘t37’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta38’
      hyperid =

        ‘24038’

      name =

        ‘theta38’

      short.name =

        ‘t38’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta39’
      hyperid =

        ‘24039’

      name =

        ‘theta39’

      short.name =

        ‘t39’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta40’
      hyperid =

        ‘24040’

      name =

        ‘theta40’

      short.name =

        ‘t40’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta41’
      hyperid =

        ‘24041’

      name =

        ‘theta41’

      short.name =

        ‘t41’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta42’
      hyperid =

        ‘24042’

      name =

        ‘theta42’

      short.name =

        ‘t42’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta43’
      hyperid =

        ‘24043’

      name =

        ‘theta43’

      short.name =

        ‘t43’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta44’
      hyperid =

        ‘24044’

      name =

        ‘theta44’

      short.name =

        ‘t44’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta45’
      hyperid =

        ‘24045’

      name =

        ‘theta45’

      short.name =

        ‘t45’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta46’
      hyperid =

        ‘24046’

      name =

        ‘theta46’

      short.name =

        ‘t46’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta47’
      hyperid =

        ‘24047’

      name =

        ‘theta47’

      short.name =

        ‘t47’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta48’
      hyperid =

        ‘24048’

      name =

        ‘theta48’

      short.name =

        ‘t48’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta49’
      hyperid =

        ‘24049’

      name =

        ‘theta49’

      short.name =

        ‘t49’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta50’
      hyperid =

        ‘24050’

      name =

        ‘theta50’

      short.name =

        ‘t50’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta51’
      hyperid =

        ‘24051’

      name =

        ‘theta51’

      short.name =

        ‘t51’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta52’
      hyperid =

        ‘24052’

      name =

        ‘theta52’

      short.name =

        ‘t52’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta53’
      hyperid =

        ‘24053’

      name =

        ‘theta53’

      short.name =

        ‘t53’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta54’
      hyperid =

        ‘24054’

      name =

        ‘theta54’

      short.name =

        ‘t54’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta55’
      hyperid =

        ‘24055’

      name =

        ‘theta55’

      short.name =

        ‘t55’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta56’
      hyperid =

        ‘24056’

      name =

        ‘theta56’

      short.name =

        ‘t56’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta57’
      hyperid =

        ‘24057’

      name =

        ‘theta57’

      short.name =

        ‘t57’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta58’
      hyperid =

        ‘24058’

      name =

        ‘theta58’

      short.name =

        ‘t58’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta59’
      hyperid =

        ‘24059’

      name =

        ‘theta59’

      short.name =

        ‘t59’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta60’
      hyperid =

        ‘24060’

      name =

        ‘theta60’

      short.name =

        ‘t60’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta61’
      hyperid =

        ‘24061’

      name =

        ‘theta61’

      short.name =

        ‘t61’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta62’
      hyperid =

        ‘24062’

      name =

        ‘theta62’

      short.name =

        ‘t62’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta63’
      hyperid =

        ‘24063’

      name =

        ‘theta63’

      short.name =

        ‘t63’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta64’
      hyperid =

        ‘24064’

      name =

        ‘theta64’

      short.name =

        ‘t64’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta65’
      hyperid =

        ‘24065’

      name =

        ‘theta65’

      short.name =

        ‘t65’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta66’
      hyperid =

        ‘24066’

      name =

        ‘theta66’

      short.name =

        ‘t66’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta67’
      hyperid =

        ‘24067’

      name =

        ‘theta67’

      short.name =

        ‘t67’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta68’
      hyperid =

        ‘24068’

      name =

        ‘theta68’

      short.name =

        ‘t68’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta69’
      hyperid =

        ‘24069’

      name =

        ‘theta69’

      short.name =

        ‘t69’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta70’
      hyperid =

        ‘24070’

      name =

        ‘theta70’

      short.name =

        ‘t70’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta71’
      hyperid =

        ‘24071’

      name =

        ‘theta71’

      short.name =

        ‘t71’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta72’
      hyperid =

        ‘24072’

      name =

        ‘theta72’

      short.name =

        ‘t72’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta73’
      hyperid =

        ‘24073’

      name =

        ‘theta73’

      short.name =

        ‘t73’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta74’
      hyperid =

        ‘24074’

      name =

        ‘theta74’

      short.name =

        ‘t74’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta75’
      hyperid =

        ‘24075’

      name =

        ‘theta75’

      short.name =

        ‘t75’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta76’
      hyperid =

        ‘24076’

      name =

        ‘theta76’

      short.name =

        ‘t76’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta77’
      hyperid =

        ‘24077’

      name =

        ‘theta77’

      short.name =

        ‘t77’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta78’
      hyperid =

        ‘24078’

      name =

        ‘theta78’

      short.name =

        ‘t78’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta79’
      hyperid =

        ‘24079’

      name =

        ‘theta79’

      short.name =

        ‘t79’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta80’
      hyperid =

        ‘24080’

      name =

        ‘theta80’

      short.name =

        ‘t80’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta81’
      hyperid =

        ‘24081’

      name =

        ‘theta81’

      short.name =

        ‘t81’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta82’
      hyperid =

        ‘24082’

      name =

        ‘theta82’

      short.name =

        ‘t82’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta83’
      hyperid =

        ‘24083’

      name =

        ‘theta83’

      short.name =

        ‘t83’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta84’
      hyperid =

        ‘24084’

      name =

        ‘theta84’

      short.name =

        ‘t84’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta85’
      hyperid =

        ‘24085’

      name =

        ‘theta85’

      short.name =

        ‘t85’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta86’
      hyperid =

        ‘24086’

      name =

        ‘theta86’

      short.name =

        ‘t86’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta87’
      hyperid =

        ‘24087’

      name =

        ‘theta87’

      short.name =

        ‘t87’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta88’
      hyperid =

        ‘24088’

      name =

        ‘theta88’

      short.name =

        ‘t88’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta89’
      hyperid =

        ‘24089’

      name =

        ‘theta89’

      short.name =

        ‘t89’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta90’
      hyperid =

        ‘24090’

      name =

        ‘theta90’

      short.name =

        ‘t90’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta91’
      hyperid =

        ‘24091’

      name =

        ‘theta91’

      short.name =

        ‘t91’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta92’
      hyperid =

        ‘24092’

      name =

        ‘theta92’

      short.name =

        ‘t92’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta93’
      hyperid =

        ‘24093’

      name =

        ‘theta93’

      short.name =

        ‘t93’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta94’
      hyperid =

        ‘24094’

      name =

        ‘theta94’

      short.name =

        ‘t94’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta95’
      hyperid =

        ‘24095’

      name =

        ‘theta95’

      short.name =

        ‘t95’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta96’
      hyperid =

        ‘24096’

      name =

        ‘theta96’

      short.name =

        ‘t96’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta97’
      hyperid =

        ‘24097’

      name =

        ‘theta97’

      short.name =

        ‘t97’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta98’
      hyperid =

        ‘24098’

      name =

        ‘theta98’

      short.name =

        ‘t98’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta99’
      hyperid =

        ‘24099’

      name =

        ‘theta99’

      short.name =

        ‘t99’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta100’
      hyperid =

        ‘24100’

      name =

        ‘theta100’

      short.name =

        ‘t100’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘A SPDE3 model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘spde3’

  Model ‘iid1d’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘25001’

      name =

        ‘precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘wishart1d’

      param =

        ‘2 1e-04’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Gaussian random effect in dim=1 with Wishart prior’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘iid123d’

  Model ‘iid2d’.

    Number of hyperparmeters are 3.

    Hyperparameter ‘theta1’
      hyperid =

        ‘26001’

      name =

        ‘log precision1’

      short.name =

        ‘prec1’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘wishart2d’

      param =

        ‘4 1 1 0’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘26002’

      name =

        ‘log precision2’

      short.name =

        ‘prec2’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘26003’

      name =

        ‘logit correlation’

      short.name =

        ‘cor’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Gaussian random effect in dim=2 with Wishart prior’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘TRUE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘1 2’

      n.div.by =

        ‘2’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘iid123d’

  Model ‘iid3d’.

    Number of hyperparmeters are 6.

    Hyperparameter ‘theta1’
      hyperid =

        ‘27001’

      name =

        ‘log precision1’

      short.name =

        ‘prec1’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘wishart3d’

      param =

        ‘7 1 1 1 0 0 0’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘27002’

      name =

        ‘log precision2’

      short.name =

        ‘prec2’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘27003’

      name =

        ‘log precision3’

      short.name =

        ‘prec3’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘27004’

      name =

        ‘logit correlation12’

      short.name =

        ‘cor12’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta5’
      hyperid =

        ‘27005’

      name =

        ‘logit correlation13’

      short.name =

        ‘cor13’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta6’
      hyperid =

        ‘27006’

      name =

        ‘logit correlation23’

      short.name =

        ‘cor23’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Gaussian random effect in dim=3 with Wishart prior’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘TRUE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘1 2 3’

      n.div.by =

        ‘3’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘iid123d’

  Model ‘iid4d’.

    Number of hyperparmeters are 10.

    Hyperparameter ‘theta1’
      hyperid =

        ‘28001’

      name =

        ‘log precision1’

      short.name =

        ‘prec1’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘wishart4d’

      param =

        ‘11 1 1 1 1 0 0 0 0 0 0’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘28002’

      name =

        ‘log precision2’

      short.name =

        ‘prec2’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘28003’

      name =

        ‘log precision3’

      short.name =

        ‘prec3’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘28004’

      name =

        ‘log precision4’

      short.name =

        ‘prec4’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘28005’

      name =

        ‘logit correlation12’

      short.name =

        ‘cor12’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta6’
      hyperid =

        ‘28006’

      name =

        ‘logit correlation13’

      short.name =

        ‘cor13’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta7’
      hyperid =

        ‘28007’

      name =

        ‘logit correlation14’

      short.name =

        ‘cor14’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta8’
      hyperid =

        ‘28008’

      name =

        ‘logit correlation23’

      short.name =

        ‘cor23’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta9’
      hyperid =

        ‘28009’

      name =

        ‘logit correlation24’

      short.name =

        ‘cor24’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta10’
      hyperid =

        ‘28010’

      name =

        ‘logit correlation34’

      short.name =

        ‘cor34’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Gaussian random effect in dim=4 with Wishart prior’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘TRUE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘1 2 3 4’

      n.div.by =

        ‘4’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘iid123d’

  Model ‘iid5d’.

    Number of hyperparmeters are 15.

    Hyperparameter ‘theta1’
      hyperid =

        ‘29001’

      name =

        ‘log precision1’

      short.name =

        ‘prec1’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘wishart5d’

      param =

        ‘16 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘29002’

      name =

        ‘log precision2’

      short.name =

        ‘prec2’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘29003’

      name =

        ‘log precision3’

      short.name =

        ‘prec3’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘29004’

      name =

        ‘log precision4’

      short.name =

        ‘prec4’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘29005’

      name =

        ‘log precision5’

      short.name =

        ‘prec5’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘29006’

      name =

        ‘logit correlation12’

      short.name =

        ‘cor12’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta7’
      hyperid =

        ‘29007’

      name =

        ‘logit correlation13’

      short.name =

        ‘cor13’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta8’
      hyperid =

        ‘29008’

      name =

        ‘logit correlation14’

      short.name =

        ‘cor14’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta9’
      hyperid =

        ‘29009’

      name =

        ‘logit correlation15’

      short.name =

        ‘cor15’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta10’
      hyperid =

        ‘29010’

      name =

        ‘logit correlation23’

      short.name =

        ‘cor23’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta11’
      hyperid =

        ‘29011’

      name =

        ‘logit correlation24’

      short.name =

        ‘cor24’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta12’
      hyperid =

        ‘29012’

      name =

        ‘logit correlation25’

      short.name =

        ‘cor25’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta13’
      hyperid =

        ‘29013’

      name =

        ‘logit correlation34’

      short.name =

        ‘cor34’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta14’
      hyperid =

        ‘29014’

      name =

        ‘logit correlation35’

      short.name =

        ‘cor35’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta15’
      hyperid =

        ‘29015’

      name =

        ‘logit correlation45’

      short.name =

        ‘cor45’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Gaussian random effect in dim=5 with Wishart prior’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘TRUE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘1 2 3 4 5’

      n.div.by =

        ‘5’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘iid123d’

  Model ‘2diid’.

    Number of hyperparmeters are 3.

    Hyperparameter ‘theta1’
      hyperid =

        ‘30001’

      name =

        ‘log precision1’

      short.name =

        ‘prec1’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘30002’

      name =

        ‘log precision2’

      short.name =

        ‘prec2’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘30003’

      name =

        ‘correlation’

      short.name =

        ‘cor’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 0.15’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘(This model is obsolute)’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘1 2’

      n.div.by =

        ‘2’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘iid123d’

  Model ‘z’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘31001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The z-model in a classical mixed model formulation’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘z’

      status =

        ‘experimental’

  Model ‘rw2d’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘32001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Thin-plate spline model’

      constr =

        ‘TRUE’

      nrow.ncol =

        ‘TRUE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘rw2d’

  Model ‘rw2diid’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘33001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘pc.prec’

      param =

        ‘1 0.01’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘33002’

      name =

        ‘logit phi’

      short.name =

        ‘phi’

      prior =

        ‘pc’

      param =

        ‘0.5 0.5’

      initial =

        ‘3’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Thin-plate spline with iid noise’

      constr =

        ‘TRUE’

      nrow.ncol =

        ‘TRUE’

      augmented =

        ‘TRUE’

      aug.factor =

        ‘2’

      aug.constr =

        ‘2’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘TRUE’

      status =

        ‘experimental’

      pdf =

        ‘rw2diid’

  Model ‘slm’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘34001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘34002’

      name =

        ‘rho’

      short.name =

        ‘rho’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 10’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Spatial lag model’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘TRUE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘slm’

      status =

        ‘experimental’

  Model ‘matern2d’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘35001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘35002’

      name =

        ‘log range’

      short.name =

        ‘range’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 0.01’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Matern covariance function on a regular grid’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘TRUE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘TRUE’

      pdf =

        ‘matern2d’

  Model ‘copy’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘36001’

      name =

        ‘beta’

      short.name =

        ‘b’

      initial =

        ‘1’

      fixed =

        ‘TRUE’

      prior =

        ‘normal’

      param =

        ‘1 10’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Create a copy of a model component’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘NA’

  Model ‘clinear’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘37001’

      name =

        ‘beta’

      short.name =

        ‘b’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘1 10’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Constrained linear effect’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘clinear’

  Model ‘sigm’.

    Number of hyperparmeters are 3.

    Hyperparameter ‘theta1’
      hyperid =

        ‘38001’

      name =

        ‘beta’

      short.name =

        ‘b’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘1 10’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘38002’

      name =

        ‘loghalflife’

      short.name =

        ‘halflife’

      initial =

        ‘3’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘3 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘38003’

      name =

        ‘logshape’

      short.name =

        ‘shape’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘10 10’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Sigmoidal effect of a covariate’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      status =

        ‘experimental’

      pdf =

        ‘sigm’

  Model ‘revsigm’.

    Number of hyperparmeters are 3.

    Hyperparameter ‘theta1’
      hyperid =

        ‘39001’

      name =

        ‘beta’

      short.name =

        ‘b’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘1 10’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘39002’

      name =

        ‘loghalflife’

      short.name =

        ‘halflife’

      initial =

        ‘3’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘3 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘39003’

      name =

        ‘logshape’

      short.name =

        ‘shape’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘10 10’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Reverse sigmoidal effect of a covariate’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      status =

        ‘experimental’

      pdf =

        ‘sigm’

  Model ‘log1exp’.

    Number of hyperparmeters are 3.

    Hyperparameter ‘theta1’
      hyperid =

        ‘39011’

      name =

        ‘beta’

      short.name =

        ‘b’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘39012’

      name =

        ‘alpha’

      short.name =

        ‘a’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘39013’

      name =

        ‘gamma’

      short.name =

        ‘g’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘A nonlinear model of a covariate’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      status =

        ‘experimental’

      pdf =

        ‘log1exp’

  Model ‘logdist’.

    Number of hyperparmeters are 3.

    Hyperparameter ‘theta1’
      hyperid =

        ‘39021’

      name =

        ‘beta’

      short.name =

        ‘b’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘39022’

      name =

        ‘alpha1’

      short.name =

        ‘a1’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘0.1 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘39023’

      name =

        ‘alpha2’

      short.name =

        ‘a2’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘0.1 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A nonlinear model of a covariate’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      status =

        ‘experimental’

      pdf =

        ‘logdist’

Section ‘group’.

  Valid models in this section are:

  Model ‘exchangeable’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘40001’

      name =

        ‘logit correlation’

      short.name =

        ‘rho’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Exchangeable correlations’

  Model ‘exchangeablepos’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘40101’

      name =

        ‘logit correlation’

      short.name =

        ‘rho’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.5’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Exchangeable positive correlations’

  Model ‘ar1’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘41001’

      name =

        ‘logit correlation’

      short.name =

        ‘rho’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 0.15’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘AR(1) correlations’

  Model ‘ar’.

    Number of hyperparmeters are 11.

    Hyperparameter ‘theta1’
      hyperid =

        ‘42001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      prior =

        ‘pc.prec’

      param =

        ‘3 0.01’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘42002’

      name =

        ‘pacf1’

      short.name =

        ‘pacf1’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.5’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta3’
      hyperid =

        ‘42003’

      name =

        ‘pacf2’

      short.name =

        ‘pacf2’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.4’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta4’
      hyperid =

        ‘42004’

      name =

        ‘pacf3’

      short.name =

        ‘pacf3’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.3’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta5’
      hyperid =

        ‘42005’

      name =

        ‘pacf4’

      short.name =

        ‘pacf4’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta6’
      hyperid =

        ‘42006’

      name =

        ‘pacf5’

      short.name =

        ‘pacf5’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta7’
      hyperid =

        ‘42007’

      name =

        ‘pacf6’

      short.name =

        ‘pacf6’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta8’
      hyperid =

        ‘42008’

      name =

        ‘pacf7’

      short.name =

        ‘pacf7’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta9’
      hyperid =

        ‘42009’

      name =

        ‘pacf8’

      short.name =

        ‘pacf8’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta10’
      hyperid =

        ‘42010’

      name =

        ‘pacf9’

      short.name =

        ‘pacf9’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta11’
      hyperid =

        ‘42011’

      name =

        ‘pacf10’

      short.name =

        ‘pacf10’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.cor0’

      param =

        ‘0.5 0.1’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘AR(p) correlations’

  Model ‘rw1’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘43001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Random walk of order 1’

  Model ‘rw2’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘44001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Random walk of order 2’

  Model ‘besag’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘45001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Besag model’

  Model ‘iid’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘46001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Independent model’

Section ‘mix’.

  Valid models in this section are:

  Model ‘gaussian’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘47001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      prior =

        ‘loggamma’

      param =

        ‘1 0.01’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Gaussian mixture’

Section ‘link’.

  Valid models in this section are:

  Model ‘default’.

    Number of hyperparmeters are 0.

  Model ‘cloglog’.

    Number of hyperparmeters are 0.

  Model ‘loglog’.

    Number of hyperparmeters are 0.

  Model ‘identity’.

    Number of hyperparmeters are 0.

  Model ‘inverse’.

    Number of hyperparmeters are 0.

  Model ‘log’.

    Number of hyperparmeters are 0.

  Model ‘neglog’.

    Number of hyperparmeters are 0.

  Model ‘logit’.

    Number of hyperparmeters are 0.

  Model ‘probit’.

    Number of hyperparmeters are 0.

  Model ‘cauchit’.

    Number of hyperparmeters are 0.

  Model ‘tan’.

    Number of hyperparmeters are 0.

  Model ‘quantile’.

    Number of hyperparmeters are 0.

  Model ‘sslogit’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘48001’

      name =

        ‘sensitivity’

      short.name =

        ‘sens’

      prior =

        ‘logitbeta’

      param =

        ‘10 5’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta2’
      hyperid =

        ‘48002’

      name =

        ‘specificity’

      short.name =

        ‘spec’

      prior =

        ‘logitbeta’

      param =

        ‘10 5’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Logit link with sensitivity and specificity’

      pdf =

        ‘NA’

  Model ‘logoffset’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘49001’

      name =

        ‘beta’

      short.name =

        ‘b’

      prior =

        ‘normal’

      param =

        ‘0 100’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Log-link with an offset’

      pdf =

        ‘logoffset’

  Model ‘logitoffset’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘49011’

      name =

        ‘prob’

      short.name =

        ‘p’

      prior =

        ‘normal’

      param =

        ‘-1 100’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Logit-link with an offset’

      status =

        ‘experimental’

      pdf =

        ‘logitoffset’

  Model ‘test1’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘50001’

      name =

        ‘beta’

      short.name =

        ‘b’

      prior =

        ‘normal’

      param =

        ‘0 100’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘A test1-link function (experimental)’

      pdf =

        ‘NA’

  Model ‘special1’.

    Number of hyperparmeters are 11.

    Hyperparameter ‘theta1’
      hyperid =

        ‘51001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘51002’

      name =

        ‘beta1’

      short.name =

        ‘beta1’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘mvnorm’

      param =

        ‘0 100’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘51003’

      name =

        ‘beta2’

      short.name =

        ‘beta2’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘51004’

      name =

        ‘beta3’

      short.name =

        ‘beta3’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘51005’

      name =

        ‘beta4’

      short.name =

        ‘beta4’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘51006’

      name =

        ‘beta5’

      short.name =

        ‘beta5’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta7’
      hyperid =

        ‘51007’

      name =

        ‘beta6’

      short.name =

        ‘beta6’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta8’
      hyperid =

        ‘51008’

      name =

        ‘beta7’

      short.name =

        ‘beta7’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta9’
      hyperid =

        ‘51009’

      name =

        ‘beta8’

      short.name =

        ‘beta8’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta10’
      hyperid =

        ‘51010’

      name =

        ‘beta9’

      short.name =

        ‘beta9’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta11’
      hyperid =

        ‘51011’

      name =

        ‘beta10’

      short.name =

        ‘beta10’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘A special1-link function (experimental)’

      pdf =

        ‘NA’

  Model ‘special2’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘52001’

      name =

        ‘beta’

      short.name =

        ‘b’

      prior =

        ‘normal’

      param =

        ‘0 10’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘A special2-link function (experimental)’

      pdf =

        ‘NA’

Section ‘predictor’.

  Valid models in this section are:

  Model ‘predictor’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘53001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘12’

      fixed =

        ‘TRUE’

      prior =

        ‘loggamma’

      param =

        ‘1 1e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘(not used)’

Section ‘hazard’.

  Valid models in this section are:

  Model ‘rw1’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘54001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A random walk of order 1 for the log-hazard’

  Model ‘rw2’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘55001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A random walk of order 2 for the log-hazard’

Section ‘likelihood’.

  Valid models in this section are:

  Model ‘poisson’.

    Number of hyperparmeters are 0.

  Model ‘contpoisson’.

    Number of hyperparmeters are 0.

  Model ‘qcontpoisson’.

    Number of hyperparmeters are 0.

  Model ‘cenpoisson’.

    Number of hyperparmeters are 0.

  Model ‘gpoisson’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘56001’

      name =

        ‘overdispersion’

      short.name =

        ‘phi’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘56002’

      name =

        ‘p’

      short.name =

        ‘p’

      initial =

        ‘1’

      fixed =

        ‘TRUE’

      prior =

        ‘normal’

      param =

        ‘1 100’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘The generalized Poisson likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default log logoffset’

      pdf =

        ‘gpoisson’

      status =

        ‘experimental’

  Model ‘binomial’.

    Number of hyperparmeters are 0.

  Model ‘testbinomial1’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘57001’

      name =

        ‘sensitivity’

      short.name =

        ‘s’

      initial =

        ‘3’

      fixed =

        ‘FALSE’

      prior =

        ‘logitbeta’

      param =

        ‘2 1’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta2’
      hyperid =

        ‘57002’

      name =

        ‘specificity’

      short.name =

        ‘e’

      initial =

        ‘3’

      fixed =

        ‘FALSE’

      prior =

        ‘logitbeta’

      param =

        ‘2 1’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘(experimental)’

      status =

        ‘experimental’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default logit cauchit probit cloglog loglog log’

      pdf =

        ‘testbinomial1’

  Model ‘pom’.

    Number of hyperparmeters are 10.

    Hyperparameter ‘theta1’
      hyperid =

        ‘57101’

      name =

        ‘theta1’

      short.name =

        ‘theta1’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘dirichlet’

      param =

        ‘3’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘57102’

      name =

        ‘theta2’

      short.name =

        ‘theta2’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘57103’

      name =

        ‘theta3’

      short.name =

        ‘theta3’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘57104’

      name =

        ‘theta4’

      short.name =

        ‘theta4’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘57105’

      name =

        ‘theta5’

      short.name =

        ‘theta5’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘57106’

      name =

        ‘theta6’

      short.name =

        ‘theta6’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta7’
      hyperid =

        ‘57107’

      name =

        ‘theta7’

      short.name =

        ‘theta7’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta8’
      hyperid =

        ‘57108’

      name =

        ‘theta8’

      short.name =

        ‘theta8’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta9’
      hyperid =

        ‘57109’

      name =

        ‘theta9’

      short.name =

        ‘theta9’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta10’
      hyperid =

        ‘57110’

      name =

        ‘theta10’

      short.name =

        ‘theta10’

      initial =

        ‘NA’

      fixed =

        ‘FALSE’

      prior =

        ‘none’

      param =

        ''

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Likelihood for the proportional odds model’

      status =

        ‘experimental’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default identity’

      pdf =

        ‘pom’

  Model ‘gamma’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘58001’

      name =

        ‘precision parameter’

      short.name =

        ‘prec’

      initial =

        ‘4.60517018598809’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 0.01’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Gamma likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log quantile’

      pdf =

        ‘gamma’

  Model ‘gammacount’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘59001’

      name =

        ‘log alpha’

      short.name =

        ‘alpha’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.gammacount’

      param =

        ‘3’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A Gamma generalisation of the Poisson likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      status =

        ‘experimental’

      pdf =

        ‘gammacount’

  Model ‘qkumar’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘60001’

      name =

        ‘precision parameter’

      short.name =

        ‘prec’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 0.001’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A quantile version of the Kumar likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit cauchit’

      pdf =

        ‘qkumar’

  Model ‘qloglogistic’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘60011’

      name =

        ‘log alpha’

      short.name =

        ‘alpha’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘25 25’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A quantile loglogistic likelihood’

      status =

        ‘changed:Oct.25.2017’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log neglog’

      pdf =

        ‘qloglogistic’

  Model ‘qloglogisticsurv’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘60021’

      name =

        ‘log alpha’

      short.name =

        ‘alpha’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘25 25’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A quantile loglogistic likelihood (survival)’

      status =

        ‘changed:Oct.25.2017’

      survival =

        ‘TRUE’

      discrete =

        ‘FALSE’

      link =

        ‘default log neglog’

      pdf =

        ‘qloglogistic’

  Model ‘beta’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘61001’

      name =

        ‘precision parameter’

      short.name =

        ‘phi’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 0.1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Beta likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘beta’

  Model ‘betabinomial’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘62001’

      name =

        ‘overdispersion’

      short.name =

        ‘rho’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 0.4’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘The Beta-Binomial likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘betabinomial’

  Model ‘cbinomial’.

    Number of hyperparmeters are 0.

  Model ‘nbinomial’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘63001’

      name =

        ‘size’

      short.name =

        ‘size’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The negBinomial likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default log logoffset quantile’

      pdf =

        ‘nbinomial’

  Model ‘simplex’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘64001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The simplex likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘simplex’

  Model ‘gaussian’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘65001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Gaussian likelihoood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity logit cauchit log logoffset’

      pdf =

        ‘gaussian’

  Model ‘normal’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘66001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Gaussian likelihoood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘gaussian’

  Model ‘circularnormal’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘67001’

      name =

        ‘log precision parameter’

      short.name =

        ‘prec’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 0.01’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The circular Gaussian likelihoood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default tan’

      pdf =

        ‘circular-normal’

      status =

        ‘experimental’

  Model ‘wrappedcauchy’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘68001’

      name =

        ‘log precision parameter’

      short.name =

        ‘prec’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 0.005’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘The wrapped Cauchy likelihoood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default tan’

      pdf =

        ‘wrapped-cauchy’

      status =

        ‘disabled’

  Model ‘iidgamma’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘69001’

      name =

        ‘logshape’

      short.name =

        ‘shape’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘100 100’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘69002’

      name =

        ‘lograte’

      short.name =

        ‘rate’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘100 100’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘(experimental)’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘iidgamma’

      status =

        ‘experimental’

  Model ‘iidlogitbeta’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘70001’

      name =

        ‘log.a’

      short.name =

        ‘a’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘70002’

      name =

        ‘log.b’

      short.name =

        ‘b’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘(experimental)’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit’

      pdf =

        ‘iidlogitbeta’

      status =

        ‘experimental’

  Model ‘loggammafrailty’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘71001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘(experimental)’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘loggammafrailty’

      status =

        ‘experimental’

  Model ‘logistic’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘72001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Logistic likelihoood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘logistic’

  Model ‘skewnormal’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘73001’

      name =

        ‘inverse.scale’

      short.name =

        ‘iscale’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

    Hyperparameter ‘theta2’
      hyperid =

        ‘73002’

      name =

        ‘skewness’

      short.name =

        ‘skew’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 10’

    Properties:
      doc =

        ‘The Skew-Normal likelihoood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘sn’

  Model ‘sn’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘74001’

      name =

        ‘log inverse scale’

      short.name =

        ‘iscale’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

    Hyperparameter ‘theta2’
      hyperid =

        ‘74002’

      name =

        ‘logit skewness’

      short.name =

        ‘skew’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 10’

      to.theta =

        '`function(x, shape.max = 1) log((1+x/shape.max)/(1-x/shape.max))`'

      from.theta =

        '`function(x, shape.max = 1) shape.max*(2*exp(x)/(1+exp(x))-1)`'

    Properties:
      doc =

        ‘The Skew-Normal likelihoood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘sn’

  Model ‘sn2’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘75001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

    Hyperparameter ‘theta2’
      hyperid =

        ‘75002’

      name =

        ‘logit skewness’

      short.name =

        ‘skew’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 10’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘The Skew-Normal likelihoood (alt param)’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      status =

        ‘experimental’

      pdf =

        ‘sn2’

  Model ‘gev’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘76001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘76002’

      name =

        ‘gev parameter’

      short.name =

        ‘gev’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 25’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘The Generalized Extreme Value likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      status =

        ‘experimental’

      pdf =

        ‘gev’

  Model ‘lognormal’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘77101’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The log-Normal likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘lognormal’

  Model ‘lognormalsurv’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘78001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The log-Normal likelihood (survival)’

      survival =

        ‘TRUE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘lognormal’

  Model ‘exponential’.

    Number of hyperparmeters are 0.

  Model ‘exponentialsurv’.

    Number of hyperparmeters are 0.

  Model ‘coxph’.

    Number of hyperparmeters are 0.

  Model ‘weibull’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘79001’

      name =

        ‘log alpha’

      short.name =

        ‘alpha’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘25 25’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Weibull likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log neglog quantile’

      pdf =

        ‘weibull’

  Model ‘weibullsurv’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘79101’

      name =

        ‘log alpha’

      short.name =

        ‘alpha’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘25 25’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Weibull likelihood (survival)’

      survival =

        ‘TRUE’

      discrete =

        ‘FALSE’

      link =

        ‘default log neglog quantile’

      pdf =

        ‘weibull’

  Model ‘loglogistic’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘80001’

      name =

        ‘log alpha’

      short.name =

        ‘alpha’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘25 25’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The loglogistic likelihood’

      status =

        ‘changed:Oct.25.2017’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log neglog’

      pdf =

        ‘loglogistic’

  Model ‘loglogisticsurv’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘80011’

      name =

        ‘log alpha’

      short.name =

        ‘alpha’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘25 25’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The loglogistic likelihood (survival)’

      status =

        ‘changed:Oct.25.2017’

      survival =

        ‘TRUE’

      discrete =

        ‘FALSE’

      link =

        ‘default log neglog’

      pdf =

        ‘loglogistic’

  Model ‘weibullcure’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘81001’

      name =

        ‘log alpha’

      short.name =

        ‘a’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘25 25’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘81002’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘The Weibull-cure likelihood (survival)’

      survival =

        ‘TRUE’

      discrete =

        ‘FALSE’

      link =

        ‘default log neglog’

      pdf =

        ‘weibullcure’

  Model ‘stochvol’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘82001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘500’

      fixed =

        ‘TRUE’

      prior =

        ‘loggamma’

      param =

        ‘1 0.005’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘The Gaussian stochvol likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘stochvolgaussian’

  Model ‘stochvolt’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘83001’

      name =

        ‘log degrees of freedom’

      short.name =

        ‘dof’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.dof’

      param =

        ‘15 0.5’

      to.theta =

        '`function(x) log(x-2)`'

      from.theta =

        '`function(x) 2+exp(x)`'

    Properties:
      doc =

        ‘The Student-t stochvol likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘stochvolt’

  Model ‘stochvolnig’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘84001’

      name =

        ‘skewness’

      short.name =

        ‘skew’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 10’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘84002’

      name =

        ‘shape’

      short.name =

        ‘shape’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 0.5’

      to.theta =

        '`function(x) log(x-1)`'

      from.theta =

        '`function(x) 1+exp(x)`'

    Properties:
      doc =

        ‘The Normal inverse Gaussian stochvol likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘stochvolnig’

  Model ‘zeroinflatedpoisson0’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘85001’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Zero-inflated Poisson, type 0’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatedpoisson1’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘86001’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Zero-inflated Poisson, type 1’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatedpoisson2’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘87001’

      name =

        ‘log alpha’

      short.name =

        ‘a’

      initial =

        ‘0.693147180559945’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0.693147180559945 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Zero-inflated Poisson, type 2’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatedbetabinomial0’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘88001’

      name =

        ‘overdispersion’

      short.name =

        ‘rho’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 0.4’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta2’
      hyperid =

        ‘88002’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Zero-inflated Beta-Binomial, type 0’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatedbetabinomial1’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘89001’

      name =

        ‘overdispersion’

      short.name =

        ‘rho’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 0.4’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta2’
      hyperid =

        ‘89002’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Zero-inflated Beta-Binomial, type 1’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatedbinomial0’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘90001’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Zero-inflated Binomial, type 0’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatedbinomial1’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘91001’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Zero-inflated Binomial, type 1’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatedbinomial2’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘92001’

      name =

        ‘alpha’

      short.name =

        ‘alpha’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Zero-inflated Binomial, type 2’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘zeroinflated’

  Model ‘zeroninflatedbinomial2’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘93001’

      name =

        ‘alpha1’

      short.name =

        ‘alpha1’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘93002’

      name =

        ‘alpha2’

      short.name =

        ‘alpha2’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Zero and N inflated binomial, type 2’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘NA’

  Model ‘zeroninflatedbinomial3’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘93101’

      name =

        ‘alpha0’

      short.name =

        ‘alpha0’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘93102’

      name =

        ‘alphaN’

      short.name =

        ‘alphaN’

      initial =

        ‘1’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Zero and N inflated binomial, type 3’

      status =

        ‘experimental’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatedbetabinomial2’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘94001’

      name =

        ‘log alpha’

      short.name =

        ‘a’

      initial =

        ‘0.693147180559945’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0.693147180559945 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘94002’

      name =

        ‘beta’

      short.name =

        ‘b’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘0 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Zero inflated Beta-Binomial, type 2’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default logit cauchit probit cloglog loglog’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatednbinomial0’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘95001’

      name =

        ‘log size’

      short.name =

        ‘size’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘95002’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Zero inflated negBinomial, type 0’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatednbinomial1’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘96001’

      name =

        ‘log size’

      short.name =

        ‘size’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘96002’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Zero inflated negBinomial, type 1’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatednbinomial1strata2’.

    Number of hyperparmeters are 11.

    Hyperparameter ‘theta1’
      hyperid =

        ‘97001’

      name =

        ‘log size’

      short.name =

        ‘size’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘97002’

      name =

        ‘logit probability 1’

      short.name =

        ‘prob1’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta3’
      hyperid =

        ‘97003’

      name =

        ‘logit probability 2’

      short.name =

        ‘prob2’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta4’
      hyperid =

        ‘97004’

      name =

        ‘logit probability 3’

      short.name =

        ‘prob3’

      initial =

        ‘-1’

      fixed =

        ‘TRUE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta5’
      hyperid =

        ‘97005’

      name =

        ‘logit probability 4’

      short.name =

        ‘prob4’

      initial =

        ‘-1’

      fixed =

        ‘TRUE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta6’
      hyperid =

        ‘97006’

      name =

        ‘logit probability 5’

      short.name =

        ‘prob5’

      initial =

        ‘-1’

      fixed =

        ‘TRUE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta7’
      hyperid =

        ‘97007’

      name =

        ‘logit probability 6’

      short.name =

        ‘prob6’

      initial =

        ‘-1’

      fixed =

        ‘TRUE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta8’
      hyperid =

        ‘97008’

      name =

        ‘logit probability 7’

      short.name =

        ‘prob7’

      initial =

        ‘-1’

      fixed =

        ‘TRUE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta9’
      hyperid =

        ‘97009’

      name =

        ‘logit probability 8’

      short.name =

        ‘prob8’

      initial =

        ‘-1’

      fixed =

        ‘TRUE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta10’
      hyperid =

        ‘97010’

      name =

        ‘logit probability 9’

      short.name =

        ‘prob9’

      initial =

        ‘-1’

      fixed =

        ‘TRUE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta11’
      hyperid =

        ‘97011’

      name =

        ‘logit probability 10’

      short.name =

        ‘prob10’

      initial =

        ‘-1’

      fixed =

        ‘TRUE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Properties:
      doc =

        ‘Zero inflated negBinomial, type 1, strata 2’

      status =

        ‘experimental’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatednbinomial1strata3’.

    Number of hyperparmeters are 11.

    Hyperparameter ‘theta1’
      hyperid =

        ‘98001’

      name =

        ‘logit probability’

      short.name =

        ‘prob’

      initial =

        ‘-1’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘-1 0.2’

      to.theta =

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

      from.theta =

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

    Hyperparameter ‘theta2’
      hyperid =

        ‘98002’

      name =

        ‘log size 1’

      short.name =

        ‘size1’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘98003’

      name =

        ‘log size 2’

      short.name =

        ‘size2’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘98004’

      name =

        ‘log size 3’

      short.name =

        ‘size3’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘TRUE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘98005’

      name =

        ‘log size 4’

      short.name =

        ‘size4’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘TRUE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘98006’

      name =

        ‘log size 5’

      short.name =

        ‘size5’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘TRUE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta7’
      hyperid =

        ‘98007’

      name =

        ‘log size 6’

      short.name =

        ‘size6’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘TRUE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta8’
      hyperid =

        ‘98008’

      name =

        ‘log size 7’

      short.name =

        ‘size7’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘TRUE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta9’
      hyperid =

        ‘98009’

      name =

        ‘log size 8’

      short.name =

        ‘size8’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘TRUE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta10’
      hyperid =

        ‘98010’

      name =

        ‘log size 9’

      short.name =

        ‘size9’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘TRUE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta11’
      hyperid =

        ‘98011’

      name =

        ‘log size 10’

      short.name =

        ‘size10’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘TRUE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Zero inflated negBinomial, type 1, strata 3’

      status =

        ‘experimental’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘zeroinflated’

  Model ‘zeroinflatednbinomial2’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘99001’

      name =

        ‘log size’

      short.name =

        ‘size’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.mgamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘99002’

      name =

        ‘log alpha’

      short.name =

        ‘a’

      initial =

        ‘0.693147180559945’

      fixed =

        ‘FALSE’

      prior =

        ‘gaussian’

      param =

        ‘2 1’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Zero inflated negBinomial, type 2’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default log’

      pdf =

        ‘zeroinflated’

  Model ‘t’.

    Number of hyperparmeters are 2.

    Hyperparameter ‘theta1’
      hyperid =

        ‘100001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘100002’

      name =

        ‘log degrees of freedom’

      short.name =

        ‘dof’

      initial =

        ‘5’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.dof’

      param =

        ‘15 0.5’

      to.theta =

        '`function(x) log(x-2)`'

      from.theta =

        '`function(x) 2+exp(x)`'

    Properties:
      doc =

        ‘Student-t likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘student-t’

  Model ‘tstrata’.

    Number of hyperparmeters are 11.

    Hyperparameter ‘theta1’
      hyperid =

        ‘101001’

      name =

        ‘log degrees of freedom’

      short.name =

        ‘dof’

      initial =

        ‘4’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.dof’

      param =

        ‘15 0.5’

      to.theta =

        '`function(x) log(x-5)`'

      from.theta =

        '`function(x) 5+exp(x)`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘101002’

      name =

        ‘log precision1’

      short.name =

        ‘prec1’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘101003’

      name =

        ‘log precision2’

      short.name =

        ‘prec2’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘101004’

      name =

        ‘log precision3’

      short.name =

        ‘prec3’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘101005’

      name =

        ‘log precision4’

      short.name =

        ‘prec4’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘101006’

      name =

        ‘log precision5’

      short.name =

        ‘prec5’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta7’
      hyperid =

        ‘101007’

      name =

        ‘log precision6’

      short.name =

        ‘prec6’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta8’
      hyperid =

        ‘101008’

      name =

        ‘log precision7’

      short.name =

        ‘prec7’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta9’
      hyperid =

        ‘101009’

      name =

        ‘log precision8’

      short.name =

        ‘prec8’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta10’
      hyperid =

        ‘101010’

      name =

        ‘log precision9’

      short.name =

        ‘prec9’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Hyperparameter ‘theta11’
      hyperid =

        ‘101011’

      name =

        ‘log precision10’

      short.name =

        ‘prec10’

      initial =

        ‘2’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘A stratified version of the Student-t likelihood’

      survival =

        ‘FALSE’

      discrete =

        ‘FALSE’

      link =

        ‘default identity’

      pdf =

        ‘tstrata’

  Model ‘nmix’.

    Number of hyperparmeters are 10.

    Hyperparameter ‘theta1’
      hyperid =

        ‘101101’

      name =

        ‘beta1’

      short.name =

        ‘beta1’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 0.5’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘101102’

      name =

        ‘beta2’

      short.name =

        ‘beta2’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘101103’

      name =

        ‘beta3’

      short.name =

        ‘beta3’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘101104’

      name =

        ‘beta4’

      short.name =

        ‘beta4’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘101105’

      name =

        ‘beta5’

      short.name =

        ‘beta5’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘101106’

      name =

        ‘beta6’

      short.name =

        ‘beta6’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta7’
      hyperid =

        ‘101107’

      name =

        ‘beta7’

      short.name =

        ‘beta7’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta8’
      hyperid =

        ‘101108’

      name =

        ‘beta8’

      short.name =

        ‘beta8’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta9’
      hyperid =

        ‘101109’

      name =

        ‘beta9’

      short.name =

        ‘beta9’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta10’
      hyperid =

        ‘101110’

      name =

        ‘beta10’

      short.name =

        ‘beta10’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Properties:
      doc =

        ‘Binomial-Poisson mixture’

      status =

        ‘experimental’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default logit probit’

      pdf =

        ‘nmix’

  Model ‘nmixnb’.

    Number of hyperparmeters are 11.

    Hyperparameter ‘theta1’
      hyperid =

        ‘101121’

      name =

        ‘beta1’

      short.name =

        ‘beta1’

      initial =

        ‘2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 0.5’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta2’
      hyperid =

        ‘101122’

      name =

        ‘beta2’

      short.name =

        ‘beta2’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta3’
      hyperid =

        ‘101123’

      name =

        ‘beta3’

      short.name =

        ‘beta3’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta4’
      hyperid =

        ‘101124’

      name =

        ‘beta4’

      short.name =

        ‘beta4’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta5’
      hyperid =

        ‘101125’

      name =

        ‘beta5’

      short.name =

        ‘beta5’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta6’
      hyperid =

        ‘101126’

      name =

        ‘beta6’

      short.name =

        ‘beta6’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta7’
      hyperid =

        ‘101127’

      name =

        ‘beta7’

      short.name =

        ‘beta7’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta8’
      hyperid =

        ‘101128’

      name =

        ‘beta8’

      short.name =

        ‘beta8’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta9’
      hyperid =

        ‘101129’

      name =

        ‘beta9’

      short.name =

        ‘beta9’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta10’
      hyperid =

        ‘101130’

      name =

        ‘beta10’

      short.name =

        ‘beta10’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘normal’

      param =

        ‘0 1’

      to.theta =

        '`function(x) x`'

      from.theta =

        '`function(x) x`'

    Hyperparameter ‘theta11’
      hyperid =

        ‘101131’

      name =

        ‘overdispersion’

      short.name =

        ‘overdispersion’

      initial =

        ‘0’

      fixed =

        ‘FALSE’

      prior =

        ‘pc.gamma’

      param =

        ‘7’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘NegBinomial-Poisson mixture’

      status =

        ‘experimental’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default logit probit’

      pdf =

        ‘nmixnb’

  Model ‘gp’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘101201’

      name =

        ‘shape’

      short.name =

        ‘xi’

      initial =

        ‘-2.30258509299405’

      fixed =

        ‘FALSE’

      prior =

        ‘loggamma’

      param =

        ‘1 15’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘Generalized Pareto likelihood’

      status =

        ‘experimental’

      survival =

        ‘FALSE’

      discrete =

        ‘TRUE’

      link =

        ‘default quantile’

      pdf =

        ‘genPareto’

  Model ‘logperiodogram’.

    Number of hyperparmeters are 0.

Section ‘prior’.

  Valid models in this section are:

  Model ‘normal’.

    Number of parameters in the prior = 2

  Model ‘gaussian’.

    Number of parameters in the prior = 2

  Model ‘wishart1d’.

    Number of parameters in the prior = 2

  Model ‘wishart2d’.

    Number of parameters in the prior = 4

  Model ‘wishart3d’.

    Number of parameters in the prior = 7

  Model ‘wishart4d’.

    Number of parameters in the prior = 11

  Model ‘wishart5d’.

    Number of parameters in the prior = 16

  Model ‘loggamma’.

    Number of parameters in the prior = 2

  Model ‘minuslogsqrtruncnormal’.

    Number of parameters in the prior = 2

  Model ‘logtnormal’.

    Number of parameters in the prior = 2

  Model ‘logtgaussian’.

    Number of parameters in the prior = 2

  Model ‘flat’.

    Number of parameters in the prior = 0

  Model ‘logflat’.

    Number of parameters in the prior = 0

  Model ‘logiflat’.

    Number of parameters in the prior = 0

  Model ‘mvnorm’.

    Number of parameters in the prior = -1

  Model ‘pc.ar’.

    Number of parameters in the prior = 1

  Model ‘dirichlet’.

    Number of parameters in the prior = 1

  Model ‘none’.

    Number of parameters in the prior = 0

  Model ‘invalid’.

    Number of parameters in the prior = 0

  Model ‘betacorrelation’.

    Number of parameters in the prior = 2

  Model ‘logitbeta’.

    Number of parameters in the prior = 2

  Model ‘pc.prec’.

    Number of parameters in the prior = 2

  Model ‘pc.dof’.

    Number of parameters in the prior = 2

  Model ‘pc.cor0’.

    Number of parameters in the prior = 2

  Model ‘pc.cor1’.

    Number of parameters in the prior = 2

  Model ‘pc.fgnh’.

    Number of parameters in the prior = 2

  Model ‘pc.spde.GA’.

    Number of parameters in the prior = 4

  Model ‘pc.matern’.

    Number of parameters in the prior = 3

  Model ‘pc.range’.

    Number of parameters in the prior = 2

  Model ‘pc.gamma’.

    Number of parameters in the prior = 1

  Model ‘pc.mgamma’.

    Number of parameters in the prior = 1

  Model ‘pc.gammacount’.

    Number of parameters in the prior = 1

  Model ‘pc’.

    Number of parameters in the prior = 2

  Model ‘ref.ar’.

    Number of parameters in the prior = 0

  Model ‘pom’.

    Number of parameters in the prior = 0

  Model ‘jeffreystdf’.

    Number of parameters in the prior = 0

  Model ‘expression:’.

    Number of parameters in the prior = -1

  Model ‘table:’.

    Number of parameters in the prior = -1

Section ‘wrapper’.

  Valid models in this section are:

  Model ‘joint’.

    Number of hyperparmeters are 1.

    Hyperparameter ‘theta’
      hyperid =

        ‘102001’

      name =

        ‘log precision’

      short.name =

        ‘prec’

      initial =

        ‘0’

      fixed =

        ‘TRUE’

      prior =

        ‘loggamma’

      param =

        ‘1 5e-05’

      to.theta =

        '`function(x) log(x)`'

      from.theta =

        '`function(x) exp(x)`'

    Properties:
      doc =

        ‘(experimental)’

      constr =

        ‘FALSE’

      nrow.ncol =

        ‘FALSE’

      augmented =

        ‘FALSE’

      aug.factor =

        ‘1’

      aug.constr =

        ‘NULL’

      n.div.by =

        ‘NULL’

      n.required =

        ‘FALSE’

      set.default.values =

        ‘FALSE’

      pdf =

        ‘NA’

### Examples

    ## How to set hyperparameters to pass as the argument 'hyper'. This
    ## format is compatible with the old style (using 'initial', 'fixed',
    ## 'prior', 'param'), but the new style using 'hyper' take preceedence
    ## 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)))

[Package INLA version 18.07.12 Index]