Valid models in INLA

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

inla.models()

Value

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

Section `latent'.

Valid models in this section are:

Model `linear'.

Number of hyperparmeters are 0.

Model `iid'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`1001'

name =

`log precision'

short.name =

`prec'

prior =

`loggamma'

param =

`1 5e-05'

initial =

`4'

fixed =

`FALSE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`Gaussian random effects in dim=1'

constr =

`FALSE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`1'

aug.constr =

`NULL'

n.div.by =

`NULL'

n.required =

`FALSE'

set.default.values =

`FALSE'

pdf =

`indep'

Model `mec'.

Number of hyperparmeters are 4.

Hyperparameter `theta1'

hyperid =

`2001'

name =

`beta'

short.name =

`b'

prior =

`gaussian'

param =

`1 0.001'

initial =

`1'

fixed =

`FALSE'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta2'

hyperid =

`2002'

name =

`prec.u'

short.name =

`prec'

prior =

`loggamma'

param =

`1 1e-04'

initial =

`9.21034037197618'

fixed =

`TRUE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Hyperparameter `theta3'

hyperid =

`2003'

name =

`mean.x'

short.name =

`mu.x'

prior =

`gaussian'

param =

`0 1e-04'

initial =

`0'

fixed =

`TRUE'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta4'

hyperid =

`2004'

name =

`prec.x'

short.name =

`prec.x'

prior =

`loggamma'

param =

`1 10000'

initial =

`-9.21034037197618'

fixed =

`TRUE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`Classical measurement error model'

constr =

`FALSE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`1'

aug.constr =

`NULL'

n.div.by =

`NULL'

n.required =

`FALSE'

set.default.values =

`FALSE'

pdf =

`mec'

Model `meb'.

Number of hyperparmeters are 2.

Hyperparameter `theta1'

hyperid =

`3001'

name =

`beta'

short.name =

`b'

prior =

`gaussian'

param =

`1 0.001'

initial =

`1'

fixed =

`FALSE'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta2'

hyperid =

`3002'

name =

`prec.u'

short.name =

`prec'

prior =

`loggamma'

param =

`1 1e-04'

initial =

`6.90775527898214'

fixed =

`FALSE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`Berkson measurement error model'

constr =

`FALSE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`1'

aug.constr =

`NULL'

n.div.by =

`NULL'

n.required =

`FALSE'

set.default.values =

`FALSE'

pdf =

`meb'

Model `rgeneric'.

Number of hyperparmeters are 0.

Model `rw1'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`4001'

name =

`log precision'

short.name =

`prec'

prior =

`loggamma'

param =

`1 5e-05'

initial =

`4'

fixed =

`FALSE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`Random walk of order 1'

constr =

`TRUE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`1'

aug.constr =

`NULL'

n.div.by =

`NULL'

n.required =

`FALSE'

set.default.values =

`FALSE'

min.diff =

`1e-05'

pdf =

`rw1'

Model `rw2'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`5001'

name =

`log precision'

short.name =

`prec'

prior =

`loggamma'

param =

`1 5e-05'

initial =

`4'

fixed =

`FALSE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`Random walk of order 2'

constr =

`TRUE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`1'

aug.constr =

`NULL'

n.div.by =

`NULL'

n.required =

`FALSE'

set.default.values =

`FALSE'

min.diff =

`0.001'

pdf =

`rw2'

Model `crw2'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`6001'

name =

`log precision'

short.name =

`prec'

prior =

`loggamma'

param =

`1 5e-05'

initial =

`4'

fixed =

`FALSE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`Exact solution to the random walk of order 2'

constr =

`TRUE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`2'

aug.constr =

`1'

n.div.by =

`NULL'

n.required =

`FALSE'

set.default.values =

`FALSE'

min.diff =

`0.001'

pdf =

`crw2'

Model `seasonal'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`7001'

name =

`log precision'

short.name =

`prec'

prior =

`loggamma'

param =

`1 5e-05'

initial =

`4'

fixed =

`FALSE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`Seasonal model for time series'

constr =

`FALSE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`1'

aug.constr =

`NULL'

n.div.by =

`NULL'

n.required =

`FALSE'

set.default.values =

`FALSE'

pdf =

`seasonal'

Model `besag'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`8001'

name =

`log precision'

short.name =

`prec'

prior =

`loggamma'

param =

`1 5e-05'

initial =

`4'

fixed =

`FALSE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`The Besag area model (CAR-model)'

constr =

`TRUE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`1'

aug.constr =

`NULL'

n.div.by =

`NULL'

n.required =

`TRUE'

set.default.values =

`TRUE'

pdf =

`besag'

Model `besag2'.

Number of hyperparmeters are 2.

Hyperparameter `theta1'

hyperid =

`9001'

name =

`log precision'

short.name =

`prec'

prior =

`loggamma'

param =

`1 5e-05'

initial =

`4'

fixed =

`FALSE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Hyperparameter `theta2'

hyperid =

`9002'

name =

`scaling parameter'

short.name =

`a'

prior =

`loggamma'

param =

`10 10'

initial =

`0'

fixed =

`FALSE'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`The shared Besag model'

constr =

`TRUE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`1'

aug.constr =

`1 2'

n.div.by =

`2'

n.required =

`TRUE'

set.default.values =

`TRUE'

pdf =

`besag2'

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 =

`TRUE'

prior =

`normal'

param =

`1 36'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta6'

hyperid =

`16106'

name =

`gamma3'

short.name =

`g3'

initial =

`1'

fixed =

`TRUE'

prior =

`normal'

param =

`1 36'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta7'

hyperid =

`16107'

name =

`gamma4'

short.name =

`g4'

initial =

`1'

fixed =

`TRUE'

prior =

`normal'

param =

`1 36'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta8'

hyperid =

`16108'

name =

`gamma5'

short.name =

`g5'

initial =

`1'

fixed =

`TRUE'

prior =

`normal'

param =

`1 36'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta9'

hyperid =

`16109'

name =

`gamma6'

short.name =

`g6'

initial =

`1'

fixed =

`TRUE'

prior =

`normal'

param =

`1 36'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta10'

hyperid =

`16110'

name =

`gamma7'

short.name =

`g7'

initial =

`1'

fixed =

`TRUE'

prior =

`normal'

param =

`1 36'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta11'

hyperid =

`16111'

name =

`gamma8'

short.name =

`g8'

initial =

`1'

fixed =

`TRUE'

prior =

`normal'

param =

`1 36'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta12'

hyperid =

`16112'

name =

`gamma9'

short.name =

`g9'

initial =

`1'

fixed =

`TRUE'

prior =

`normal'

param =

`1 36'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta13'

hyperid =

`16113'

name =

`gamma10'

short.name =

`g10'

initial =

`1'

fixed =

`TRUE'

prior =

`normal'

param =

`1 36'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

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 `loga'.

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'

status =

`disabled'

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 `sn'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`49031'

name =

`alpha'

short.name =

`alpha'

initial =

`0'

fixed =

`TRUE'

prior =

`pc.sn'

param =

`50'

to.theta =

`function(x, amax3 = 3.2^3) log((1+x/amax3)/(1-x/amax3))'

from.theta =

`function(x, amax3 = 3.2^3) amax3*(2*exp(x)/(1+exp(x))-1)'

Properties:

doc =

`Skew-normal link'

status =

`experimental'

pdf =

`linksn'

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 `xpoisson'.

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 `xbinomial'.

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 `bgev'.

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 =

`xi'

initial =

`-4'

fixed =

`FALSE'

prior =

`pc.gevtail'

param =

`7 0 0.5'

to.theta =

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

from.theta =

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

Hyperparameter `theta3'

hyperid =

`57203'

name =

`beta1'

short.name =

`beta1'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta4'

hyperid =

`57204'

name =

`beta2'

short.name =

`beta2'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta5'

hyperid =

`57205'

name =

`beta3'

short.name =

`beta3'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta6'

hyperid =

`57206'

name =

`beta4'

short.name =

`beta4'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta7'

hyperid =

`57207'

name =

`beta5'

short.name =

`beta5'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta8'

hyperid =

`57208'

name =

`beta6'

short.name =

`beta6'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta9'

hyperid =

`57209'

name =

`beta7'

short.name =

`beta7'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta10'

hyperid =

`57210'

name =

`beta8'

short.name =

`beta8'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta11'

hyperid =

`57211'

name =

`beta9'

short.name =

`beta9'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Hyperparameter `theta12'

hyperid =

`57212'

name =

`beta10'

short.name =

`beta'

initial =

`NA'

fixed =

`FALSE'

prior =

`normal'

param =

`0 300'

to.theta =

`function(x) x'

from.theta =

`function(x) x'

Properties:

doc =

`The blended Generalized Extreme Value likelihood'

status =

`experimental'

survival =

`FALSE'

discrete =

`FALSE'

link =

`default identity log'

pdf =

`bgev'

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 =

`1'

fixed =

`FALSE'

prior =

`loggamma'

param =

`1 0.1'

to.theta =

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

from.theta =

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

Properties:

doc =

`A quantile version of the Kumar likelihood'

survival =

`FALSE'

discrete =

`FALSE'

link =

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

pdf =

`betabinomial'

Model `betabinomialna'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`62101'

name =

`overdispersion'

short.name =

`rho'

initial =

`0'

fixed =

`FALSE'

prior =

`gaussian'

param =

`0 0.4'

to.theta =

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

from.theta =

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

Properties:

doc =

`The Beta-Binomial Normal approximation likelihood'

survival =

`FALSE'

discrete =

`TRUE'

link =

`default logit loga cauchit probit cloglog loglog robit sn'

pdf =

`betabinomialna'

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 loga 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 loga 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 loga'

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 loga cauchit probit cloglog loglog robit sn'

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 loga cauchit probit cloglog loglog robit sn'

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 loga cauchit probit cloglog loglog robit sn'

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 loga cauchit probit cloglog loglog robit sn'

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 loga cauchit probit cloglog loglog robit sn'

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 loga cauchit probit cloglog loglog robit sn'

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 loga cauchit probit cloglog loglog robit sn'

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 loga cauchit probit cloglog loglog robit sn'

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 loga 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 loga probit'

pdf =

`nmixnb'

Model `gp'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`101201'

name =

`tail'

short.name =

`xi'

initial =

`-4'

fixed =

`FALSE'

prior =

`pc.gevtail'

param =

`7 0 0.5'

to.theta =

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

from.theta =

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

Properties:

doc =

`Generalized Pareto likelihood'

status =

`experimental'

survival =

`FALSE'

discrete =

`TRUE'

link =

`default quantile'

pdf =

`genPareto'

Model `dgp'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`101201'

name =

`tail'

short.name =

`xi'

initial =

`2'

fixed =

`FALSE'

prior =

`pc.gevtail'

param =

`7 0 0.5'

to.theta =

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

from.theta =

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

Properties:

doc =

`Discrete generalized Pareto likelihood'

status =

`experimental'

survival =

`FALSE'

discrete =

`TRUE'

link =

`default quantile'

pdf =

`dgp'

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.sn'.

Number of parameters in the prior = 1

Model `pc.gamma'.

Number of parameters in the prior = 1

Model `pc.mgamma'.

Number of parameters in the prior = 1

Model `pc.gammacount'.

Number of parameters in the prior = 1

Model `pc.gevtail'.

Number of parameters in the prior = 3

Model `pc'.

Number of parameters in the prior = 2

Model `ref.ar'.

Number of parameters in the prior = 0

Model `pom'.

Number of parameters in the prior = 0

Model `jeffreystdf'.

Number of parameters in the prior = 0

Model `expression:'.

Number of parameters in the prior = -1

Model `table:'.

Number of parameters in the prior = -1

Section `wrapper'.

Valid models in this section are:

Model `joint'.

Number of hyperparmeters are 1.

Hyperparameter `theta'

hyperid =

`102001'

name =

`log precision'

short.name =

`prec'

initial =

`0'

fixed =

`TRUE'

prior =

`loggamma'

param =

`1 5e-05'

to.theta =

`function(x) log(x)'

from.theta =

`function(x) exp(x)'

Properties:

doc =

`(experimental)'

constr =

`FALSE'

nrow.ncol =

`FALSE'

augmented =

`FALSE'

aug.factor =

`1'

aug.constr =

`NULL'

n.div.by =

`NULL'

n.required =

`FALSE'

set.default.values =

`FALSE'

pdf =

`NA'

Examples

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

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