R/RcppExports.R

In dina: Bayesian Estimation of DINA Model

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

#' Update attributes and latent class probabilities
#'
#' Update attributes and latent class probabilities by sampling from full
#' conditional distribution.
#'
#' @param Amat   A \eqn{C \times K}{C x K} `matrix` of latent classes.
#' @param Q      A \eqn{N \times K}{N x K} `matrix` indicating which
#'               skills are required for which items.
#' @param ss     A \eqn{J} `vector` of item slipping parameters.
#' @param gs     A \eqn{J} `vector` of item guessing parameters.
#' @param Y      A \eqn{N \times J}{N x J} `matrix` of observed responses.
#' @param PIs    A \eqn{C} `vector` of latent class probabilities.
#' @param ALPHAS A \eqn{N \times K}{N x K} `matrix` of latent attributes.
#' @param delta0 A \eqn{J} `vector` of Dirichlet prior parameters.
#'
#' @return
#' A \eqn{N \times K}{N x K} `matrix` of attributes and a C `vector` of
#' class probabilities.
#'
#' @author
#' Steven Andrew Culpepper
#'
#' @noRd
update_alpha <- function(Amat, Q, ss, gs, Y, PIs, ALPHAS, delta0) {
    .Call(`_dina_update_alpha`, Amat, Q, ss, gs, Y, PIs, ALPHAS, delta0)
}

#' Update item parameters
#'
#' Update guessing and slipping parameters from full conditional distribution.
#'
#' @param Y      A N by J `matrix` of observed responses.
#' @param Q      A N by K `matrix` indicating which skills are required 
#'               for which items. 
#' @param ALPHAS A N by K `matrix` of latent attributes. 
#' @param ss_old A J `vector` of item slipping parameters from prior iteration.
#' @param as0    Slipping prior alpha parameter for Beta distribution.
#' @param bs0    Slipping prior beta parameter for Beta distribution. 
#' @param ag0    Guessing prior alpha parameter for Beta distribution. 
#' @param bg0    Guessing prior beta parameter for Beta distribution.
#'
#' @return
#' A `list` with two J `vectors` of guessing and slipping parameters.
#'
#' @author Steven Andrew Culpepper
#' @noRd
update_sg <- function(Y, Q, ALPHAS, ss_old, as0, bs0, ag0, bg0) {
    .Call(`_dina_update_sg`, Y, Q, ALPHAS, ss_old, as0, bs0, ag0, bg0)
}

#' Generate Posterior Distribution with Gibbs sampler
#'
#' Function for sampling parameters from full conditional distributions.
#' The function returns a list of arrays or matrices with parameter posterior
#' samples. Note that the output includes the posterior samples in objects.
#'
#' @param Y            A \eqn{N \times J}{N x J} `matrix` of observed
#'                     responses. 
#' @param Amat         A \eqn{C \times K}{C x K} `matrix` of latent
#'                     classes. 
#' @param Q            A \eqn{N \times K}{N x K} `matrix` indicating
#'                     which skills are required for which items. 
#' @param chain_length Number of MCMC iterations.
#'
#' @return
#' A `list` with samples from the posterior distribution with each
#' entry named:
#'
#' - `CLASSES` = individual attribute profiles,
#' - `PIs` = latent class proportions,
#' - `SigS` = item slipping parameters, and
#' - `GamS` = item guessing parameters.
#'
#' @author
#' Steven Andrew Culpepper
#'
#' @seealso
#' [simcdm::sim_dina_items()] and [simcdm::attribute_classes()]
#'
#' @noRd
DINA_Gibbs_cpp <- function(Y, Q, chain_length = 10000L) {
    .Call(`_dina_DINA_Gibbs_cpp`, Y, Q, chain_length)
}