R/RcppExports.R

In bistablehistory: Cumulative History Analysis for Bistable Perception Time Series

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

#' Computes cumulative history
#'
#' Computes cumulative history based on common \code{history} values and
#' \code{normalized_tau} and \code{mixed_state} that are defined for each
#' random cluster / individual.
#'
#' @param df DataFrame with \code{"state"} (integer, 1 and 2 clear state, 3 - mixed state), \code{"duration"} (double),
#'   \code{"irandom"} (integer, 1-based index of a random cluster), \code{"run_start"} (integer, 1 for the first entry of
#'   the run, 0 otherwise), \code{"session_tmean"} (double)
#' @param normalized_tau DoubleVector A normalized tau value for each random cluster / individual. Thus, its length must be
#'   equal to the number of unique indexes in \code{df["irandom"]}.
#' @param mixed_state DoubleVector A values used for the mixed state for each random cluster / individual.
#'   Thus, its length must be equal to the number of unique indexes in \code{df["irandom"]}.
#' @param history_init DoubleVector, size 2. Initial values of history for a run.
#' @return NumericMatrix, size \code{df.nrows()} × 2. Computed history values for each state.
#' @export
#'
#' @examples
#' df <- preprocess_data(br_singleblock, state="State", duration="Duration")
#' fast_history_compute(df, 1, 0.5, c(0, 0))
fast_history_compute <- function(df, normalized_tau, mixed_state, history_init) {
    .Call(`_bistablehistory_fast_history_compute`, df, normalized_tau, mixed_state, history_init)
}

#' Computes prediction for a each sample.
#'
#' Computing prediction for each sample,
#' recomputing cumulative history and uses
#' fitted parameter values.
#'
#' @param family int, distribution family: gamma (1), lognormal(2), or
#' normal (3).
#' @param fixedN int, number of fixed parameters (>= 0).
#' @param randomN int, number of random factors (>= 1).
#' @param lmN int, number of linear models (>= 1).
#' @param istate IntegerVector, zero-based perceptual state 0 or 1,
#' 2 is mixed state.
#' @param duration DoubleVector, duration of a dominance phase.
#' @param is_used IntegerVector, whether dominance phase is used for
#' prediction (1) or not (0).
#' @param run_start IntegerVector, 1 whenever a new run starts.
#' @param session_tmean DoubleVector, average dominance phase duration.
#' @param irandom IntegerVector, zero-based index of a random effect.
#' @param fixed NumericMatrix, matrix with fixed effect values.
#' @param tau_ind NumericMatrix, matrix with samples of tau for each
#' random level.
#' @param mixed_state_ind NumericMatrix, matrix with samples of
#' mixed_state for each random level.
#' @param history_init DoubleVector, Initial values of history for a run
#' @param a NumericMatrix, matrix with samples of
#' a (intercept) for each random level.
#' @param bH NumericMatrix, matrix with sample of
#' bH for each linear model and random level.
#' @param bF NumericMatrix, matrix with sample of
#' bF for each linear model and fixed factor.
#' @param sigma DoubleVector, samples of sigma.
#'
#' @return NumericMatrix with predicted durations for each sample.
#' @export
predict_samples <- function(family, fixedN, randomN, lmN, istate, duration, is_used, run_start, session_tmean, irandom, fixed, tau_ind, mixed_state_ind, history_init, a, bH, bF, sigma) {
    .Call(`_bistablehistory_predict_samples`, family, fixedN, randomN, lmN, istate, duration, is_used, run_start, session_tmean, irandom, fixed, tau_ind, mixed_state_ind, history_init, a, bH, bF, sigma)
}