Default lavaan arguments. See lavaan.

Handling of prior distributions on covariance parameters: possible values are "srs" (default) or "fa". Option "fa" is only available for target="jags".

Default prior distributions on different types of parameters, typically the result of a call to dpriors(). See the dpriors() help file for more information.

Number of desired MCMC chains.

Number of burnin iterations, NOT including the adaptive iterations.

The total number of samples to take after burnin.

The number of adaptive iterations to use at the start of the simulation.

If TRUE, the JAGS/Stan model will be written to file (in the lavExport directory). Can also supply a character string, which serves as the name of the directory to which files will be written.

A list with potential names syntax and monitor. The syntax object is a text string containing extra code to insert in the JAGS/Stan model syntax, and the monitor object is a character vector containing extra JAGS/Stan parameters to sample.

If it is a character string, the options are currently "simple", "Mplus", "prior" (default), or "jags". In the first two cases, parameter values are set as though they will be estimated via ML (see lavaan). The starting parameter value for each chain is then perturbed from the original values through the addition of random uniform noise. If "prior" is used, the starting parameter values are obtained based on the prior distributions (while also trying to ensure that the starting values will not crash the model estimation). If "jags", no starting values are specified and JAGS will choose values on its own (and this will probably crash Stan targets). You can also supply a list of starting values for each chain, where the list format can be obtained from, e.g., blavInspect(fit, "inits"). Finally, you can specify starting values in a similar way to lavaan, using the lavaan start argument (see the lavaan documentation for all the options there). In this case, you should also set inits="simple", and be aware that the same starting values will be used for each chain.

Useful only for target="jags". If "auto", parameters are sampled until convergence is achieved (via autorun.jags()). In this case, the arguments burnin and sample are passed to autorun.jags() as startburnin and startsample, respectively. Otherwise, parameters are sampled as specified by the user (or by the run.jags defaults).

Desired MCMC sampling, with "stan" (pre-compiled marginal approach) as default. Other options include "jags", "stancond", and "stanclassic", which sample latent variables and provide some greater functionality (because syntax is written "on the fly"). But they are slower and less efficient.

Should sampled latent variables (factor scores) be saved? Logical; defaults to FALSE

Labels of equality-constrained parameters that should be "approximately" equal. Can also be "intercepts", "loadings", "regressions", "means".

The prior sd (of normal distribution) to be used in approximate equality constraints. Can be one value, or (for target="stan") a numeric vector of values that is the same length as wiggle.

Should samples be drawn from the prior, instead of the posterior (target="stan" only)? Logical; defaults to FALSE

Should DIC be computed the JAGS way, in addition to the BUGS way? Logical; defaults to FALSE

A vector of length n.chains (for target "jags") or an integer (for target "stan") containing random seeds for the MCMC run. If NULL, seeds will be chosen randomly.

A list containing additional parameters passed to run.jags (or autorun.jags) or stan. See the manpage of those functions for an overview of the additional parameters that can be set.