models: Valid models in INLA
In inbo/INLA: Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

Description Usage Value Examples

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

1	inla.models()

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 =: ‘13111’
name =: ‘log precision’
short.name =: ‘prec’
prior =: ‘pc.prec’
param =: ‘3 0.01’
initial =: ‘1’
fixed =: ‘FALSE’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

hyperid =: ‘13112’
name =: ‘logit H’
short.name =: ‘H’
prior =: ‘pcfgnh’
param =: ‘0.9 0.1’
initial =: ‘2’
fixed =: ‘FALSE’
to.theta =: 'function(x) log((2*x-1)/(2*(1-x)))'
from.theta =: 'function(x) 0.5 + 0.5*exp(x)/(1+exp(x))'

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 ‘intslope’.

Number of hyperparmeters are 13.

Hyperparameter ‘theta1’

hyperid =: ‘16101’
name =: ‘log precision1’
short.name =: ‘prec1’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘wishart2d’
param =: ‘4 1 1 0’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

hyperid =: ‘16102’
name =: ‘log precision2’
short.name =: ‘prec2’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta3’

hyperid =: ‘16103’
name =: ‘logit correlation’
short.name =: ‘cor’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) log((1+x)/(1-x))'
from.theta =: 'function(x) 2*exp(x)/(1+exp(x))-1'

Hyperparameter ‘theta4’

hyperid =: ‘16104’
name =: ‘gamma1’
short.name =: ‘g1’
initial =: ‘1’
fixed =: ‘TRUE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta5’

hyperid =: ‘16105’
name =: ‘gamma2’
short.name =: ‘g2’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta6’

hyperid =: ‘16106’
name =: ‘gamma3’
short.name =: ‘g3’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta7’

hyperid =: ‘16107’
name =: ‘gamma4’
short.name =: ‘g4’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta8’

hyperid =: ‘16108’
name =: ‘gamma5’
short.name =: ‘g5’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta9’

hyperid =: ‘16109’
name =: ‘gamma6’
short.name =: ‘g6’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta10’

hyperid =: ‘16110’
name =: ‘gamma7’
short.name =: ‘g7’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta11’

hyperid =: ‘16111’
name =: ‘gamma8’
short.name =: ‘g8’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta12’

hyperid =: ‘16112’
name =: ‘gamma9’
short.name =: ‘g9’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta13’

hyperid =: ‘16113’
name =: ‘gamma10’
short.name =: ‘g10’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘1 36’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

doc =: ‘Intecept-slope model with Wishart-prior’
constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘TRUE’
status =: ‘experimental’
pdf =: ‘intslope’

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 ‘dmatern’.

Number of hyperparmeters are 3.

Hyperparameter ‘theta1’

hyperid =: ‘35101’
name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘3’
fixed =: ‘FALSE’
prior =: ‘pc.prec’
param =: ‘1 0.01’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

hyperid =: ‘35102’
name =: ‘log range’
short.name =: ‘range’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘pc.range’
param =: ‘1 0.5’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta3’

hyperid =: ‘35103’
name =: ‘log nu’
short.name =: ‘nu’
initial =: ‘-0.693147180559945’
fixed =: ‘TRUE’
prior =: ‘loggamma’
param =: ‘0.5 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

doc =: ‘Dense Matern field’
constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘TRUE’
set.default.values =: ‘TRUE’
status =: ‘experimental’
pdf =: ‘dmatern’

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 =: ‘pc.prec’
param =: ‘1 0.01’
initial =: ‘0’
fixed =: ‘FALSE’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

doc =: ‘Gaussian mixture’

Model ‘loggamma’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

hyperid =: ‘47101’
name =: ‘log precision’
short.name =: ‘prec’
prior =: ‘pc.mgamma’
param =: ‘4.8’
initial =: ‘4’
fixed =: ‘FALSE’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

doc =: ‘LogGamma mixture’

Model ‘mloggamma’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

hyperid =: ‘47201’
name =: ‘log precision’
short.name =: ‘prec’
prior =: ‘pc.mgamma’
param =: ‘4.8’
initial =: ‘4’
fixed =: ‘FALSE’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

doc =: ‘Minus-LogGamma 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 ‘pquantile’.

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 ‘robit’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

hyperid =: ‘49021’
name =: ‘log degrees of freedom’
short.name =: ‘dof’
initial =: ‘1.6094379124341’
fixed =: ‘TRUE’
prior =: ‘pc.dof’
param =: ‘50 0.5’
to.theta =: 'function(x) log(x-2)'
from.theta =: 'function(x) 2+exp(x)'

Properties:

doc =: ‘Robit link’
status =: ‘experimental’
pdf =: ‘robit’

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 ‘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 ‘gev2’.

Number of hyperparmeters are 12.

Hyperparameter ‘theta1’

hyperid =: ‘57201’
name =: ‘spread’
short.name =: ‘sd’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 3’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

hyperid =: ‘57202’
name =: ‘tail’
short.name =: ‘tail’
initial =: ‘-4’
fixed =: ‘FALSE’
prior =: ‘pc.gevtail’
param =: ‘7 0 0.5’
to.theta =: 'function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x)/(interval[2] - x))'
from.theta =: 'function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2]-interval[1]) * exp(x)/(1.0 + exp(x))'

Hyperparameter ‘theta3’

hyperid =: ‘57203’
name =: ‘beta1’
short.name =: ‘beta1’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta4’

hyperid =: ‘57204’
name =: ‘beta2’
short.name =: ‘beta2’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta5’

hyperid =: ‘57205’
name =: ‘beta3’
short.name =: ‘beta3’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta6’

hyperid =: ‘57206’
name =: ‘beta4’
short.name =: ‘beta4’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta7’

hyperid =: ‘57207’
name =: ‘beta5’
short.name =: ‘beta5’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta8’

hyperid =: ‘57208’
name =: ‘beta6’
short.name =: ‘beta6’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta9’

hyperid =: ‘57209’
name =: ‘beta7’
short.name =: ‘beta7’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta10’

hyperid =: ‘57210’
name =: ‘beta8’
short.name =: ‘beta8’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta11’

hyperid =: ‘57211’
name =: ‘beta9’
short.name =: ‘beta9’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta12’

hyperid =: ‘57212’
name =: ‘beta10’
short.name =: ‘beta’
initial =: ‘NA’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 300’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

doc =: ‘The Generalized Extreme Value likelihood (2nd variant)’
status =: ‘experimental’
survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘gev2’

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 ‘gammasurv’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

hyperid =: ‘58101’
name =: ‘precision parameter’
short.name =: ‘prec’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 0.01’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

doc =: ‘The Gamma likelihood (survival)’
survival =: ‘TRUE’
discrete =: ‘FALSE’
status =: ‘experimental’
link =: ‘default log quantile’
pdf =: ‘gammasurv’

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’
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)’
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 robit’
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 ‘nbinomial2’.

Number of hyperparmeters are 0.

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 2.

Hyperparameter ‘theta1’

hyperid =: ‘65001’
name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

hyperid =: ‘65002’
name =: ‘log precision offset’
short.name =: ‘precoffset’
initial =: ‘72.0873067782343’
fixed =: ‘TRUE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

doc =: ‘The Gaussian likelihoood’
survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity logit cauchit log logoffset’
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 =: ‘log inverse scale’
short.name =: ‘iscale’
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 =: ‘73002’
name =: ‘logit skewness’
short.name =: ‘skew’
initial =: ‘4’
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 ‘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’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

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’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

hyperid =: ‘75002’
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 (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 =: ‘tail parameter’
short.name =: ‘tail’
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 =: ‘0.1’
fixed =: ‘FALSE’
prior =: ‘pc.alphaw’
param =: ‘5’
to.theta =: 'function(x, sc = 0.1) log(x)/sc'
from.theta =: 'function(x, sc = 0.1) exp(sc*x)'

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 =: ‘0.1’
fixed =: ‘FALSE’
prior =: ‘pc.alphaw’
param =: ‘5’
to.theta =: 'function(x, sc = 0.1) log(x)/sc'
from.theta =: 'function(x, sc = 0.1) exp(sc*x)'

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’
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)’
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 =: ‘0.1’
fixed =: ‘FALSE’
prior =: ‘pc.alphaw’
param =: ‘5’
to.theta =: 'function(x, sc = 0.1) log(x)/sc'
from.theta =: 'function(x, sc = 0.1) exp(sc*x)'

Hyperparameter ‘theta2’

hyperid =: ‘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 robit’
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 robit’
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 robit’
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 robit’
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 robit’
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 robit’
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 robit’
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 robit’
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 15.

Hyperparameter ‘theta1’

hyperid =: ‘101101’
name =: ‘beta1’
short.name =: ‘beta1’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 0.5’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta2’

hyperid =: ‘101102’
name =: ‘beta2’
short.name =: ‘beta2’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta3’

hyperid =: ‘101103’
name =: ‘beta3’
short.name =: ‘beta3’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta4’

hyperid =: ‘101104’
name =: ‘beta4’
short.name =: ‘beta4’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta5’

hyperid =: ‘101105’
name =: ‘beta5’
short.name =: ‘beta5’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta6’

hyperid =: ‘101106’
name =: ‘beta6’
short.name =: ‘beta6’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta7’

hyperid =: ‘101107’
name =: ‘beta7’
short.name =: ‘beta7’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta8’

hyperid =: ‘101108’
name =: ‘beta8’
short.name =: ‘beta8’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta9’

hyperid =: ‘101109’
name =: ‘beta9’
short.name =: ‘beta9’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta10’

hyperid =: ‘101110’
name =: ‘beta10’
short.name =: ‘beta10’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta11’

hyperid =: ‘101111’
name =: ‘beta11’
short.name =: ‘beta11’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta12’

hyperid =: ‘101112’
name =: ‘beta12’
short.name =: ‘beta12’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta13’

hyperid =: ‘101113’
name =: ‘beta13’
short.name =: ‘beta13’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta14’

hyperid =: ‘101114’
name =: ‘beta14’
short.name =: ‘beta14’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta15’

hyperid =: ‘101115’
name =: ‘beta15’
short.name =: ‘beta15’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

doc =: ‘Binomial-Poisson mixture’
status =: ‘experimental’
survival =: ‘FALSE’
discrete =: ‘TRUE’
link =: ‘default logit probit’
pdf =: ‘nmix’

Model ‘nmixnb’.

Number of hyperparmeters are 16.

Hyperparameter ‘theta1’

hyperid =: ‘101121’
name =: ‘beta1’
short.name =: ‘beta1’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 0.5’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta2’

hyperid =: ‘101122’
name =: ‘beta2’
short.name =: ‘beta2’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta3’

hyperid =: ‘101123’
name =: ‘beta3’
short.name =: ‘beta3’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta4’

hyperid =: ‘101124’
name =: ‘beta4’
short.name =: ‘beta4’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta5’

hyperid =: ‘101125’
name =: ‘beta5’
short.name =: ‘beta5’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta6’

hyperid =: ‘101126’
name =: ‘beta6’
short.name =: ‘beta6’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta7’

hyperid =: ‘101127’
name =: ‘beta7’
short.name =: ‘beta7’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta8’

hyperid =: ‘101128’
name =: ‘beta8’
short.name =: ‘beta8’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta9’

hyperid =: ‘101129’
name =: ‘beta9’
short.name =: ‘beta9’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta10’

hyperid =: ‘101130’
name =: ‘beta10’
short.name =: ‘beta10’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta11’

hyperid =: ‘101131’
name =: ‘beta11’
short.name =: ‘beta11’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta12’

hyperid =: ‘101132’
name =: ‘beta12’
short.name =: ‘beta12’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta13’

hyperid =: ‘101133’
name =: ‘beta13’
short.name =: ‘beta13’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta14’

hyperid =: ‘101134’
name =: ‘beta14’
short.name =: ‘beta14’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta15’

hyperid =: ‘101135’
name =: ‘beta15’
short.name =: ‘beta15’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘normal’
param =: ‘0 1’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta16’

hyperid =: ‘101136’
name =: ‘overdispersion’
short.name =: ‘overdispersion’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘pc.gamma’
param =: ‘7’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

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 ‘gamma’.: Number of parameters in the prior = 2
Model ‘minuslogsqrtruncnormal’.: Number of parameters in the prior = 2
Model ‘logtnormal’.: Number of parameters in the prior = 2
Model ‘logtgaussian’.: Number of parameters in the prior = 2
Model ‘flat’.: Number of parameters in the prior = 0
Model ‘logflat’.: Number of parameters in the prior = 0
Model ‘logiflat’.: Number of parameters in the prior = 0
Model ‘mvnorm’.: Number of parameters in the prior = -1
Model ‘pc.alphaw’.: Number of parameters in the prior = 1
Model ‘pc.ar’.: Number of parameters in the prior = 1
Model ‘dirichlet’.: Number of parameters in the prior = 1
Model ‘none’.: Number of parameters in the prior = 0
Model ‘invalid’.: Number of parameters in the prior = 0
Model ‘betacorrelation’.: Number of parameters in the prior = 2
Model ‘logitbeta’.: Number of parameters in the prior = 2
Model ‘pc.prec’.: Number of parameters in the prior = 2
Model ‘pc.dof’.: Number of parameters in the prior = 2
Model ‘pc.cor0’.: Number of parameters in the prior = 2
Model ‘pc.cor1’.: Number of parameters in the prior = 2
Model ‘pc.fgnh’.: Number of parameters in the prior = 2
Model ‘pc.spde.GA’.: Number of parameters in the prior = 4
Model ‘pc.matern’.: Number of parameters in the prior = 3
Model ‘pc.range’.: Number of parameters in the prior = 2
Model ‘pc.gamma’.: Number of parameters in the prior = 1
Model ‘pc.mgamma’.: Number of parameters in the prior = 1
Model ‘pc.gammacount’.: Number of parameters in the prior = 1
Model ‘pc.gevtail’.: Number of parameters in the prior = 3
Model ‘pc’.: Number of parameters in the prior = 2
Model ‘ref.ar’.: Number of parameters in the prior = 0
Model ‘pom’.: Number of parameters in the prior = 0
Model ‘jeffreystdf’.: Number of parameters in the prior = 0
Model ‘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’

## 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)))

inbo/INLA documentation built on Dec. 6, 2019, 9:51 a.m.

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inbo/INLA
Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

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Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

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In inbo/INLA: Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations