The (relative or full) path to the file that specifies the MPT model (standard .eqn syntax). Note that category labels must start with a letter (different to multiTree) and match the column names of data. Alternatively, the EQN-equations can be provided within R as a character value (cf. readEQN). Note that the first line of an .eqn-file is reserved for comments and always ignored.

The (relative or full) path to the .csv file with the data (comma separated; category labels in first row). Alternatively: a data frame or matrix (rows=individuals, columns = individual category frequencies, category labels as column names)

Specifies which parameters should be (a) constant (e.g., "a=b=.5") or (b) constrained to be identical (e.g., "Do=Dn") or (c) treated as fixed effects (i.e., identical for all participants; "a=b=FE"). Either given as the path to a text file with restrictions per row or as a list of restrictions, e.g., list("D1=D2","g=0.5"). Note that numbers in .eqn-equations (e.g., d*(1-g)*.50) are directly interpreted as equality constraints.

Data that contains covariates, for which correlations with individual MPT parameters will be sampled. Either the path to a .csv file (comma-separated: rows=individuals in the same order as data; first row must contain covariate labels). Alternatively: a data frame or matrix (rows=individuals, columns = variables; covariate labels as column names). Note that in betaMPT, correlatios are computed for discrete variables that are coded numerically (in traitMPT, this can be suppressed by using predType="f")

whether to use probit-transformed MPT parameters to compute correlations (probit-values of +Inf are truncated to max(5,max(probit)); similarly for -Inf). Default for beta-MPT: MPT parameters are used on the probability scale [0,1].

Number of iterations per chain (including burnin samples). See run.jags for details.

Number of samples for burnin (samples will not be stored and removed from n.iter)

Thinning rate.

number of MCMC chains (sampled in parallel).

number of samples to compute posterior predictive p-value (see posteriorPredictive)

shape parameter(s) of Gamma-hyperdistribution for the hierarchical beta-parameters α_s and β_s (can be a named vector to provide different hyperpriors for each parameter)

rate parameter(s) of Gamma-hyperdistribution

Name of the file to with the estimates should be stored (e.g., "parEstFile.txt")

path to RData-file where to save the model including MCMC posterior samples (an object named fittedModel; e.g., posteriorFile="mcmc.RData")

number of CPUs to be used