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[Deprecated] Deprecated; use stan_sampling_opts() instead.


  cores = getOption("mc.cores", 1L),
  warmup = 250,
  samples = 2000,
  chains = 4,
  control = list(),
  save_warmup = FALSE,
  seed = as.integer(runif(1, 1, 1e+08)),
  future = FALSE,
  max_execution_time = Inf,



Number of cores to use when executing the chains in parallel, which defaults to 1 but it is recommended to set the mc.cores option to be as many processors as the hardware and RAM allow (up to the number of chains).


Numeric, defaults to 250. Number of warmup samples per chain.


Numeric, default 2000. Overall number of posterior samples. When using multiple chains iterations per chain is samples / chains.


Numeric, defaults to 4. Number of MCMC chains to use.


List, defaults to empty. control parameters to pass to underlying rstan function. By default adapt_delta = 0.95 and max_treedepth = 15 though these settings can be overwritten.


Logical, defaults to FALSE. Should warmup progress be saved.


Numeric, defaults uniform random number between 1 and 1e8. Seed of sampling process.


Logical, defaults to FALSE. Should stan chains be run in parallel using future. This allows users to have chains fail gracefully (i.e when combined with max_execution_time). Should be combined with a call to future::plan().


Numeric, defaults to Inf (seconds). If set wil kill off processing of each chain if not finished within the specified timeout. When more than 2 chains finish successfully estimates will still be returned. If less than 2 chains return within the allowed time then estimation will fail with an informative error.


Additional parameters to pass to rstan::sampling().


A list of arguments to pass to rstan::sampling().