Bayesian robust Random Covariance Model (RCM) for heirarchical graphical modeling — rcm • bayesRCM

Bayesian robust Random Covariance Model (RCM) for heirarchical graphical modeling

Usage

rcm(
  y = data_list,
  priors = NULL,
  n_samples = 100,
  n_burn = 10,
  n_cores = 4,
  n_updates = 2
)

Arguments

priors: List of prior distribution specifications, defaults to -
n_samples: Integer, number of posterior samples to obtain
n_burn: Integer, number of posterior samples to burn
n_cores: Integer, number of cores to be run in parallel - if empty/NULL will run sequentially
data_list: List of $K$ subject's residual BOLD time series array (array of V volumes of matrices in time)

Examples

rcm::example_data_list
#> Error in loadNamespace(x): there is no package called ‘rcm’
rcm(example_data_list)
#> Error in loadNamespace(x): there is no package called ‘doParallel’