par_fusion_RIS_TA_BLR: Parallel Time-adapting Rejection-Importance Sampling Monte...
In rchan26/BayesLogitFusion: Monte Carlo Fusion for Bayesian Logistic Regression

Parallel Time-adapting Rejection-Importance Sampling Monte Carlo Fusion for Bayesian Logistic Regression model

par_fusion_RIS_TA_BLR(
  N,
  dim,
  y_split,
  X_split,
  prior_means,
  prior_variances,
  time,
  m,
  C,
  power,
  precondition = FALSE,
  samples_to_fuse,
  sub_posterior_weights,
  ESS_threshold = 0.5,
  seed = NULL,
  level = 1,
  node = 1,
  n_cores = parallel::detectCores()
)

`N`	number of samples
`dim`	dimension of the predictors (= p+1)
`y_split`	list of length m, where y_split[[c]] is the y responses for sub-posterior c
`X_split`	list of length m, where X_split[[c]] is the design matrix for sub-posterior c
`prior_means`	prior for means of predictors
`prior_variances`	prior for variances of predictors
`time`	time T for fusion algorithm
`m`	number of sub-posteriors to combine
`C`	overall number of sub-posteriors
`power`	exponent of target distribution
`precondition`	boolean value determining whether or not a preconditioning matrix is to be used
`samples_to_fuse`	list of length m, where samples_to_fuse[c] contains the samples for the c-th sub-posterior
`sub_posterior_weights`	vector of length m, where sub_posterior_weights[c] is the weight for sub-posterior c
`ESS_threshold`	number between 0 and 1 defining the proportion of the number of samples that ESS needs to be lower than for resampling (i.e. resampling is carried out only when ESS < N*ESS_threshold)
`seed`	seed number - default is NULL, meaning there is no seed
`level`	indicates which level this is for the hierarchy (default 1)
`node`	indicates which node this is for the hierarchy (default 1)
`n_cores`	number of cores to use