R/RcppExports.R

In apeterson91/fhmm:

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#' finite heirarchical mixture model - C++ estimation engine
#' @param r vector of distances associatd with different BEFs
#' @param n_j matrix of integers denoting the start and length of each observations associated BEF distances
#' @param d a 1D grid of positive real values over which the differing intensities are evaluated
#' @param L number of cluster mixture components
#' @param K number of function components
#' @param J number of rows in r matrix; number of groups
#' @param mu_0 prior mean for mean parameter
#' @param kappa_0 prior variance parameter for mean normal prior
#' @param nu_0 prior degrees of freedom for variance Inv Chisq prior
#' @param sigma_0 prior scale for variance Inv-Chisq distribtuion
#' @param iter_max number of total iterations to run the sampler for
#' @param warm_up number of iterations to discard as burn-in or warm_up
#' @param thin number of iterations to thin posterior sample draws by
#' @param seed seed with which to initialize random number generator
#' @param chain used for labeling
#' @param num_posterior_samples number of posterior samples kept after thinning
fhmm_fit <- function(r, n_j, d, L, K, J, mu_0, kappa_0, nu_0, sigma_0, iter_max, warm_up, thin, seed, chain, num_posterior_samples) {
    .Call(`_fhmm_fhmm_fit`, r, n_j, d, L, K, J, mu_0, kappa_0, nu_0, sigma_0, iter_max, warm_up, thin, seed, chain, num_posterior_samples)
}

#' computes green loss function
#' @param cluster_assignment iter_total x J cluster assignment matrix
#' @param pmat J x J pairwise probability of co-clustering matrix
#' @param chain
green_loss_engine <- function(cluster_assignment, pmat, tau) {
    .Call(`_fhmm_green_loss_engine`, cluster_assignment, pmat, tau)
}