R/RcppExports.R

In RGLab/flowReMix: Generalized Linear Modeling of FCM Data With Clustered Coefficients

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

CppFlowSstepList <- function(subjectDataList, nsamp, nSubsets, intSampSize, isingCoefs, covariance, keepEach, MHcoef, betaDispersion, randomAssignProb, modelprobs, iterAssignCoef, prior, zeroPosteriorProbs, M, invcov, mixed, sampleRandom, doNotSample, markovChainEM) {
    .Call('_flowReMix_CppFlowSstepList', PACKAGE = 'flowReMix', subjectDataList, nsamp, nSubsets, intSampSize, isingCoefs, covariance, keepEach, MHcoef, betaDispersion, randomAssignProb, modelprobs, iterAssignCoef, prior, zeroPosteriorProbs, M, invcov, mixed, sampleRandom, doNotSample, markovChainEM)
}

CppFlowSstepList_mc_vec <- function(nsubjects, Y, N, subpopInd, clusterassignments, nullEta, altEta, rand, index, preassign, nsamp, nsubsets, intSampSize, isingCoefs, covariance, keepEach, MHcoef, betaDispersion, randomAssignProb, iterAssignCoef, prior, zeroPosteriorProbs, M, invcov, mixed, sampleRandom, doNotSample, markovChainEM, cpus, seed) {
    .Call('_flowReMix_CppFlowSstepList_mc_vec', PACKAGE = 'flowReMix', nsubjects, Y, N, subpopInd, clusterassignments, nullEta, altEta, rand, index, preassign, nsamp, nsubsets, intSampSize, isingCoefs, covariance, keepEach, MHcoef, betaDispersion, randomAssignProb, iterAssignCoef, prior, zeroPosteriorProbs, M, invcov, mixed, sampleRandom, doNotSample, markovChainEM, cpus, seed)
}

vecBetaBinomDens <- function(count, N, prob, M) {
    .Call('_flowReMix_vecBetaBinomDens', PACKAGE = 'flowReMix', count, N, prob, M)
}

setNumericVectorToZero <- function(x) {
    invisible(.Call('_flowReMix_setNumericVectorToZero', PACKAGE = 'flowReMix', x))
}

subsetAssignGibbs <- function(y, prop, N, isingCoefs, nullEta, altEta, covariance, nsamp, nSubsets, keepEach, intSampSize, MHcoef, popInd, unifVec, normVec, dispersion, betaDispersion, preAssignment, randomAssignProb, mprobs, preAssignCoef, prior, zeroPosteriorProbs, doNotSample, assignment, msize) {
    .Call('_flowReMix_subsetAssignGibbs', PACKAGE = 'flowReMix', y, prop, N, isingCoefs, nullEta, altEta, covariance, nsamp, nSubsets, keepEach, intSampSize, MHcoef, popInd, unifVec, normVec, dispersion, betaDispersion, preAssignment, randomAssignProb, mprobs, preAssignCoef, prior, zeroPosteriorProbs, doNotSample, assignment, msize)
}

simRandomEffectCoordinateMH <- function(y, N, i, nsamp, nSubsets, MHcoef, assignment, popInd, eta, randomEstt, condvar, covariance, invcov, MHattempts, MHsuccess, unifVec, dispersion, betaDispersion, keepEach, msize) {
    .Call('_flowReMix_simRandomEffectCoordinateMH', PACKAGE = 'flowReMix', y, N, i, nsamp, nSubsets, MHcoef, assignment, popInd, eta, randomEstt, condvar, covariance, invcov, MHattempts, MHsuccess, unifVec, dispersion, betaDispersion, keepEach, msize)
}

newMHsampler <- function(assign, random, initAssign, initRand, y, N, keepEach, prior, isingCoefs, preAssignment, invcov, covariance, condvar, dispersion, nullEta, altEta, popInd, MHattempts, MHsuccess, MHcoef) {
    invisible(.Call('_flowReMix_newMHsampler', PACKAGE = 'flowReMix', assign, random, initAssign, initRand, y, N, keepEach, prior, isingCoefs, preAssignment, invcov, covariance, condvar, dispersion, nullEta, altEta, popInd, MHattempts, MHsuccess, MHcoef))
}

weightedMean <- function(x, weights, na_rm) {
    .Call('_flowReMix_weightedMean', PACKAGE = 'flowReMix', x, weights, na_rm)
}