Fit the specified modskurt model by sampling its posterior distribution

Usage

mskt_fit(
  spec,
  use_subset = FALSE,
  seed = NULL,
  init = NULL,
  chains = 4,
  parallel_chains = getOption("mc.cores", 1),
  iter_warmup = 1000,
  iter_sampling = 1000,
  max_treedepth = NULL,
  adapt_delta = NULL,
  show_messages = TRUE,
  show_exceptions = FALSE,
  ...
)

Arguments

spec: the modskurt model specification returned by `mskt_spec`
use_subset: whether to use specified subset or all data
seed: a seed for the Stan random number generator
init: initialisation method, see ?cmdstanr::`model-method-sample`
chains: the number of Markov chains
parallel_chains: how many cores to distribute chains over
iter_warmup: number of iterations used to learn posterior curvature
iter_sampling: number of posterior samples drawn
max_treedepth, adapt_delta: tuning parameters, see details
show_messages: additional verbosity
show_exceptions: additional verbosity
...: additional arguments to ?cmdstanr::`model-method-sample`

Value

A `cmdstanr::CmdStanMCMC` object