Hidden wrapper function for sampling from ADMB models

.sample_admb(
  model,
  path = getwd(),
  iter = 2000,
  init = NULL,
  chains = 3,
  warmup = NULL,
  seeds = NULL,
  thin = 1,
  mceval = FALSE,
  duration = NULL,
  cores = NULL,
  control = NULL,
  verbose = TRUE,
  algorithm = "NUTS",
  skip_optimization = TRUE,
  skip_monitor = FALSE,
  skip_unbounded = TRUE,
  admb_args = NULL
)

Arguments

model	Name of model (i.e., 'model' for model.tpl). For non-Windows systems this will automatically be converted to './model' internally. For Windows, long file names are sometimes shortened from e.g., 'long_model_filename' to 'LONG_~1'. This should work, but will throw warnings. Please shorten the model name. See https://en.wikipedia.org/wiki/8.3_filename.
path	Path to model executable. Defaults to working directory. Often best to have model files in a separate subdirectory, particularly for parallel.
iter	The number of samples to draw.
init	A list of lists containing the initial parameter vectors, one for each chain or a function. It is strongly recommended to initialize multiple chains from dispersed points. A of NULL signifies to use the starting values present in the model (i.e., `obj$par`) for all chains.
chains	The number of chains to run.
warmup	The number of warmup iterations.
seeds	A vector of seeds, one for each chain.
thin	The thinning rate to apply to samples. Typically not used with NUTS.
mceval	Whether to run the model with `-mceval` on samples from merged chains.
duration	The number of minutes after which the model will quit running.
cores	The number of cores to use for parallel execution. Default is number available in the system minus 1. If `cores=1`, serial execution occurs (even if `chains>1`), otherwise parallel execution via package snowfall is used. For slow analyses it is recommended to set `chains`<=`cores` so each core needs to run only a single chain.
control	A list to control the sampler. See details for further use.
verbose	Flag whether to show console output (default) or suppress it completely except for warnings and errors. Works for serial or parallel execution.
algorithm	The algorithm to use, one of "NUTS" or "RWM"
skip_optimization	Whether to run the optimizer before running MCMC. This is rarely need as it is better to run it once before to get the covariance matrix, or the estimates are not needed with adaptive NUTS.
skip_monitor	Whether to skip calculating diagnostics (effective sample size, Rhat) via the `rstan::monitor` function. This can be slow for models with high dimension or many iterations. The result is used in plots and summaries so it is recommended to turn on. If model run with `skip_monitor=FALSE` you can recreate it post-hoc by setting `fit$monitor=rstan::monitor(fit$samples, fit$warmup, print=FALSE)`.
skip_unbounded	Whether to skip returning the unbounded version of the posterior samples in addition to the bounded ones. It may be advisable to set to FALSE for very large models to save space.
admb_args	A character string which gets passed to the command line, allowing finer control