R/RcppExports.R

In Rlda: Bayesian LDA for Mixed-Membership Clustering Analysis

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' @name GibbsSamplingAbundance
#' @title Gibbs Sampling for LDA Abundance with Stick-Breaking
#' @description Compute the Gibbs Sampling for LDA Abundance with Stick-Breaking
#' @param data - dataFrame with Abundance
#' @param int n_community - Number of communities
#' @param beta - NumericVector for beta (Sx1)
#' @param gamma - Hyperparameter  Beta(1,gamma)
#' @param n_gibbs - Total number of Gibbs Samples
#' @param n_burn - Number of elements to burn-in
#' @param ll_prior - Likelihood compute with Priors ?
#' @param bool display_progress=true - Should I Show the progressBar ?
#' @return List - With Theta(n_gibbs,nLocations*n_community), Phi(n_gibbs,n_community*nSpecies) and logLikelihood
NULL

#' @name GibbsSamplingAbundance
#' @title Gibbs Sampling for LDA Abundance with Stick-Breaking
#' @description Compute the Gibbs Sampling for LDA Abundance with Stick-Breaking
#' @param data - dataFrame with Abundance
#' @param int n_community - Number of communities
#' @param beta - NumericVector for beta (Sx1)
#' @param gamma - Hyperparameter  Beta(1,gamma)
#' @param n_gibbs - Total number of Gibbs Samples
#' @param ll_prior - Likelihood compute with Priors ?
#' @param bool display_progress=true - Should I Show the progressBar ?
#' @return List - With Theta(n_gibbs,nLocations*n_community), Phi(n_gibbs,n_community*nSpecies) and logLikelihood
lda_multinomial <- function(data, n_community, beta, gamma, n_gibbs, ll_prior = TRUE, display_progress = TRUE) {
    .Call(`_Rlda_lda_multinomial`, data, n_community, beta, gamma, n_gibbs, ll_prior, display_progress)
}

#' This function summarizes the z matrix into 2 K x S matrices
#' where K is the number of communities and S is the number of species.
#' The matrix "res1" holds the values for dat=1
#' The matrix "res0" holds the values for dat=0
getks <- function(z, ncommun, dat) {
    .Call(`_Rlda_getks`, z, ncommun, dat)
}

#' This function summarizes the z matrix into a L x K matrix
#' where K is the number of communities and L is the number of locations
getlk <- function(z, locid, ncommun, nloc) {
    .Call(`_Rlda_getlk`, z, locid, ncommun, nloc)
}

#' This function samples z's
samplez <- function(ltheta, l1minustheta, lphi, dat1, locid, randu, ncommun, nloc) {
    .Call(`_Rlda_samplez`, ltheta, l1minustheta, lphi, dat1, locid, randu, ncommun, nloc)
}

#' This function converts vmat into theta
convertVtoTheta <- function(vmat, prod) {
    .Call(`_Rlda_convertVtoTheta`, vmat, prod)
}

#' @name GibbsSamplingBinomial
#' @title Compute the Gibbs Sampling for LDA Binomial
#' @description Compute the Gibbs Sampling for LDA Binomial
#' @param DATA - DataFrame with Presence and Absecence (Binomial)
#' @param POP - DataFrame with Population Size (Binomial)
#' @param int n_community - Number of communities
#' @param alpha0 - Hyperparameter Beta(alpha0,alpha1)
#' @param alpha1 - Hyperparameter Beta(alpha0,alpha1)
#' @param gamma - Hyperparameter  Beta(1,gamma)
#' @param n_gibbs - Total number of Gibbs Samples
#' @param n_burn - Number of elements to burn-in
#' @param ll_prior - Likelihood compute with Priors ?
#' @param bool display_progress=true - Should I Show the progressBar ?
#' @return List - With Theta(n_gibbs,n_community*nSpecies), Phi(n_gibbs,nLocations*n_community) and logLikelihood
NULL

#' @name GibbsSamplingBinomial
#' @title Compute the Gibbs Sampling for LDA Binomial
#' @description Compute the Gibbs Sampling for LDA Binomial
#' @param DATA - DataFrame with Presence and Absecence (Binomial)
#' @param POP - DataFrame with Population Size (Binomial)
#' @param int n_community - Number of communities
#' @param alpha0 - Hyperparameter Beta(alpha0,alpha1)
#' @param alpha1 - Hyperparameter Beta(alpha0,alpha1)
#' @param gamma - Hyperparameter  Beta(1,gamma)
#' @param n_gibbs - Total number of Gibbs Samples
#' @param ll_prior - Likelihood compute with Priors ?
#' @param bool display_progress=true - Should I Show the progressBar ?
#' @return List - With Theta(n_gibbs,n_community*nSpecies), Phi(n_gibbs,nLocations*n_community) and logLikelihood
lda_binomial <- function(data, pop, n_community, alpha0, alpha1, gamma, n_gibbs, ll_prior = TRUE, display_progress = TRUE) {
    .Call(`_Rlda_lda_binomial`, data, pop, n_community, alpha0, alpha1, gamma, n_gibbs, ll_prior, display_progress)
}

lda_binomial_var <- function(data, n_community, maxit, n_obs, gamma, a_phi, b_phi, thresh, delta_elbo, m1, m0) {
    .Call(`_Rlda_lda_binomial_var`, data, n_community, maxit, n_obs, gamma, a_phi, b_phi, thresh, delta_elbo, m1, m0)
}

#' @name GibbsSamplingPresence
#' @title Gibbs Sampling for LDA Presence and Absence
#' @description Compute the Gibbs Sampling for LDA Presence and Absence
#' @param DATA - DataFrame with Presence and Absecence (Zeros and Ones)
#' @param int n_community - Number of communities
#' @param alpha0 - Hyperparameter Beta(alpha0,alpha1)
#' @param alpha1 - Hyperparameter Beta(alpha0,alpha1)
#' @param gamma - Hyperparameter  Beta(1,gamma)
#' @param n_gibbs - Total number of Gibbs Samples
#' @param n_burn - Number of elements to burn-in
#' @param ll_prior - Likelihood compute with Priors ?
#' @param bool display_progress=true - Should I Show the progressBar ?
#' @return List - With Phi(n_gibbs,n_community*nSpecies), Theta(n_gibbs,nLocations*n_community) and logLikelihood
NULL

#' @name GibbsSamplingPresence
#' @title Gibbs Sampling for LDA Presence and Absence
#' @description Compute the Gibbs Sampling for LDA Presence and Absence
#' @param DATA - DataFrame with Presence and Absecence (Zeros and Ones)
#' @param int n_community - Number of communities
#' @param alpha0 - Hyperparameter Beta(alpha0,alpha1)
#' @param alpha1 - Hyperparameter Beta(alpha0,alpha1)
#' @param gamma - Hyperparameter  Beta(1,gamma)
#' @param n_gibbs - Total number of Gibbs Samples
#' @param ll_prior - Likelihood compute with Priors ?
#' @param bool display_progress=true - Should I Show the progressBar ?
#' @return List - With Phi(n_gibbs,n_community*nSpecies), Theta(n_gibbs,nLocations*n_community) and logLikelihood
lda_bernoulli <- function(data, n_community, alpha0, alpha1, gamma, n_gibbs, ll_prior = TRUE, display_progress = TRUE) {
    .Call(`_Rlda_lda_bernoulli`, data, n_community, alpha0, alpha1, gamma, n_gibbs, ll_prior, display_progress)
}

#''[[Rcpp::export]]
NULL

convertSBtoNormal <- function(vmat, ncol, nrow, prod) {
    .Call(`_Rlda_convertSBtoNormal`, vmat, ncol, nrow, prod)
}

aggregatesum <- function(Tobesum, nind, nobs, ind) {
    .Call(`_Rlda_aggregatesum`, Tobesum, nind, nobs, ind)
}