R/dirichlet_process_mvnormal.R

In dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

#' Create a Dirichlet mixture of multivariate normal distributions.
#'
#' \eqn{G_0 (\boldsymbol{\mu} , \Lambda |  \boldsymbol{\mu _0} , \kappa _0, \nu _0, T_0)  = N ( \boldsymbol{\mu} | \boldsymbol{\mu _0} , (\kappa _0 \Lambda )^{-1} ) \mathrm{Wi} _{\nu _0} (\Lambda | T_0)}
#'
#' @param y Data
#' @param g0Priors Prior parameters for the base distribution.
#' @param alphaPriors Alpha prior parameters. See \code{\link{UpdateAlpha}}.
#' @param numInitialClusters Number of clusters to initialise with.
#' @export
DirichletProcessMvnormal <- function(y,
                                     g0Priors,
                                     alphaPriors = c(2, 4),
                                     numInitialClusters=1) {

  if(!is.matrix(y)){
    y <- matrix(y, ncol=length(y))
  }

  if(missing(g0Priors)){
    g0Priors <- list(mu0 = rep_len(0, length.out = ncol(y)),
                     Lambda = diag(ncol(y)),
                     kappa0 = ncol(y),
                     nu = ncol(y))
  }


  mdobj <- MvnormalCreate(g0Priors)
  dpobj <- DirichletProcessCreate(y, mdobj, alphaPriors)
  dpobj <- Initialise(dpobj, numInitialClusters=numInitialClusters)

  return(dpobj)
}