cpgsR: Uniform sampling in convex polytope

Documented in cpgs cpgsEquality

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

#' Complex Polytope Gibbs Sampling
#' This function draw uniform samples in a convex polytope with inequality constraints
#'
#' @param N the number of samples to generate
#' @param A a matrix
#' @param b a vector of length equals to nrow(A)
#' @param x0 a vector of length equals to nrcol(A) that should be in the polytope, for example returned by \code{\link{chebycenter}}
#' @param thin thinning interval
#'
#' @section Details:
#' This function is based on an initial matlab code developped called CPRND
#' (https://ch.mathworks.com/matlabcentral/fileexchange/34208-uniform-distribution-over-a-convex-polytope)
#' It generates samples within the complex polytope defined by \eqn{A \cdot x \leqslant   b}
#'
#' @return a matrix with one row per sample and one column per parameter
#' @examples
#' n <- 20
#' A1 <- -diag(n)
#' b1 <- as.matrix(rep(0,n))
#' A2 <- diag(n)
#' b2 <- as.matrix(rep(1,n))
#' A <- rbind(A1,A2)
#' b <- rbind(b1,b2)
#' X0 <- chebycenter(A,b)
#' x <- cpgs(1000,A,b,X0)
#' @export
#' @useDynLib cpgsR
cpgs <- function(N, A, b, x0, thin = 1L) {
    .Call('_cpgsR_cpgs', PACKAGE = 'cpgsR', N, A, b, x0, thin)
}

#' Complex Polytope Gibbs Sampling
#' This function draw uniform samples in a convex polytope with both equality and inequality constraints
#'
#' @param N the number of samples to generate
#' @param A a matrix of coefficients of inequality constants A.x<=b
#' @param b a vector of length equals to nrow(A)
#' @param C a matrix of coefficients of inequality constants C.x=v
#' @param v a vector of length equals to nrow(C)
#' @param x0 a vector of length equals to ncol(A) that should be in the polytope, for example returned by \code{\link{chebycenter}}
#' @param thin the thinning interval
#'
#' @section Details:
#' This function is based on an initial matlab code developped called CPRND
#' (https://ch.mathworks.com/matlabcentral/fileexchange/34208-uniform-distribution-over-a-convex-polytope)
#' It generates samples within the complex polytope defined by \eqn{A \cdot x \leqslant   b}
#'
#' @return a matrix with one row per sample and one column per parameter
#' @examples
#' n <- 20
#' A1 <- -diag(n)
#' b1 <- as.matrix(rep(0,n))
#' A2 <- diag(n)
#' b2 <- as.matrix(rep(1,n))
#' A <- rbind(A1,A2)
#' b <- rbind(b1,b2)
#' C <- rbind(c(1,1,rep(0,n-2)),c(0,0,1,1,rep(0,n-4)))
#' v <- matrix(rep(0.2,2),2)
#' X0 <- rep(0.1,n)
#' x <- cpgsEquality(1000,A,b,C,v,X0)
#' @export
#' @useDynLib cpgsR
cpgsEquality <- function(N, A, b, C, v, x0, thin = 1L) {
    .Call('_cpgsR_cpgsEquality', PACKAGE = 'cpgsR', N, A, b, C, v, x0, thin)
}

# Register entry points for exported C++ functions
methods::setLoadAction(function(ns) {
    .Call('_cpgsR_RcppExport_registerCCallable', PACKAGE = 'cpgsR')
})