R/RcppExports.R

In olssol/cava: Clinical Trial Design for Cancer Vaccines

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

#' PDF of single batch
#'
#' @param y response
#'
#' @export
bacBatchFreq <- function(y) {
    .Call('_cava_bacBatchFreq', PACKAGE = 'cava', y)
}

#' Probability of (n = n, r = 0:n)
#'
#' @param n sample size
#' @param bsize batch size
#' @param pmat probability matrix
#'
#' @export
bacProb <- function(n, bsize, pmat) {
    .Call('_cava_bacProb', PACKAGE = 'cava', n, bsize, pmat)
}

#' Prepare probabilities
#'
#' @param y response
#' @param nmax maximum size
#' @param nmin minimum size
#' @param bsize batch size
#'
#' @return A matrix with 2*nmax rows. Row 1-nmax is marginal P(x = r|n)
#'         Row nmax-2*nmax P(x<=r|n);
#' @export
bacCumProb <- function(y, nmax, nmin, bsize) {
    .Call('_cava_bacCumProb', PACKAGE = 'cava', y, nmax, nmin, bsize)
}

#' Single Simon 2-stage
#'
#' @param cumu cumulative binomial distribution
#' @param n1 first stage sample size
#' @param r1 first stage response rate
#' @param n total sample size
#' @param r  second stage response rate
#'
#' @export
bacSimonSingle <- function(cumu, n1, r1, n, r) {
    .Call('_cava_bacSimonSingle', PACKAGE = 'cava', cumu, n1, r1, n, r)
}

#' Simon's two-stage design
#'
#' @param y0 response 0
#' @param y1 response 1
#' @param nmax maximum size
#' @param nmin minimum size
#' @param bsize batch size
#' @param alpha observed alpha from simulations
#' @param beta observed beta from simulations.
#'
#' @export
bacSimonDesign <- function(y0, y1, nmax, nmin, bsize, alpha, beta) {
    .Call('_cava_bacSimonDesign', PACKAGE = 'cava', y0, y1, nmax, nmin, bsize, alpha, beta)
}

#' Get InterClass Correlation
#'
#' @param ys responses
#'
#' @export
bacICC <- function(ys) {
    .Call('_cava_bacICC', PACKAGE = 'cava', ys)
}

#' Get batch sizes to get the total n
#'
#' @param n sample size
#' @param bsize batch size
#'
#' @export
baBatches <- function(n, bsize) {
    .Call('_cava_baBatches', PACKAGE = 'cava', n, bsize)
}

#' Get variance given batch sizes, response rate and ICC
#'
#' @param bsizes a vector of batch sizes
#' @param p response rate 
#' @param rho inter-class correlations
#'
#' @export
baSaGetVar <- function(bsizes, p, rho) {
    .Call('_cava_baSaGetVar', PACKAGE = 'cava', bsizes, p, rho)
}

#' Get actuarial type I and II error in Sargent method
#'
#' @param bsizes a vector of batch sizes
#' @param r response rate
#' @param p0 null hypothesis
#' @param p1 alt hypothesis
#' @param rho0 rho0
#' @param rho1 rho1
#'
#' @export
baSaAlphaBeta <- function(bsizes, r, p0, p1, rho0, rho1) {
    .Call('_cava_baSaAlphaBeta', PACKAGE = 'cava', bsizes, r, p0, p1, rho0, rho1)
}

#' Get Design type I and II error using Sargent method
#' 
#' @param nmin minimum sample size
#' @param bsize batch size
#' @param alpha observed alpha from simulations
#' @param beta observed beta from simulations
#' @param p0 null hypothesis
#' @param p1 alt hypothesis
#' @param rho0 rho0
#' @param rho1 rho1
#' 
#' @export
baSaDesign <- function(nmin, bsize, alpha, beta, p0, p1, rho0, rho1) {
    .Call('_cava_baSaDesign', PACKAGE = 'cava', nmin, bsize, alpha, beta, p0, p1, rho0, rho1)
}