R/RcppExports.R

In MGDrivE: Mosquito Gene Drive Explorer

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

#' Dirichlet Distribution
#'
#' Make a single draw from a Dirichlet distribution with the shape parameter
#' one. 
#'
#' @param migrationPoint Vector of weights for draws. Must be positive.
#'
rDirichlet <- function(migrationPoint) {
    .Call('_MGDrivE_rDirichlet', PACKAGE = 'MGDrivE', migrationPoint)
}

#' Quantiles Function
#'
#' Calculate the given quantiles of a matrix.
#'
#' @usage quantileC(Trials, Probs)
#'
#' @param Trials Integer matrix to calculate quantiles over
#' @param Probs Vector of quantiles
#'
#' @details This function calculates the given quantiles over the rows of an
#' integer matrix. It uses method 8 of the stat::quantiles() function. It gives
#' the same result, to numerical accuracy, and is designed to handle matrix input.
#' It is only designed to work on integer matrices!
#'
#' @return Numeric Matrix
#'
quantileC <- function(Trials, Probs) {
    .Call('_MGDrivE_quantileC', PACKAGE = 'MGDrivE', Trials, Probs)
}

#' Calculate Geodesic Distance - Cosine Method
#'
#' This function calculates geodesic distance using the cosine method.
#'
#' @param latLongs Two column matrix of latitudes/longitudes
#' @param r Earth radius. Default is WGS-84 radius
#'
#' @examples
#' # two-column matrix with latitude/longitude, in degrees
#' latLong = cbind(runif(n = 5, min = 0, max = 90),
#'                 runif(n = 5, min = 0, max = 180))
#'
#' # cosine distance formula
#' distMat = calcCos(latLongs = latLong)
#'
#' @export
calcCos <- function(latLongs, r = 6378137) {
    .Call('_MGDrivE_calcCos', PACKAGE = 'MGDrivE', latLongs, r)
}

#' Calculate Geodesic Distance - Haversine Method
#'
#' This function calculates geodesic distance using the Haversine method.
#'
#' @param latLongs Two column matrix of latitudes/longitudes
#' @param r Earth radius. Default is WGS-84 radius
#'
#' @examples
#' # two-column matrix with latitude/longitude, in degrees
#' latLong = cbind(runif(n = 5, min = 0, max = 90),
#'                 runif(n = 5, min = 0, max = 180))
#'
#' # Haversine distance formula
#' distMat = calcHaversine(latLongs = latLong)
#'
#' @export
calcHaversine <- function(latLongs, r = 6378137) {
    .Call('_MGDrivE_calcHaversine', PACKAGE = 'MGDrivE', latLongs, r)
}

#' Calculate Geodesic Distance - Vincenty Sphere Method
#'
#' This function calculates geodesic distance using the Vincenty sphere method.
#'
#' @param latLongs Two column matrix of latitudes/longitudes
#' @param r Earth radius. Default is WGS-84 radius
#'
#' @examples
#' # two-column matrix with latitude/longitude, in degrees
#' latLong = cbind(runif(n = 5, min = 0, max = 90),
#'                 runif(n = 5, min = 0, max = 180))
#'
#' # Vincenty Sphere  distance formula
#' distMat = calcVinSph(latLongs = latLong)
#'
#' @export
calcVinSph <- function(latLongs, r = 6378137) {
    .Call('_MGDrivE_calcVinSph', PACKAGE = 'MGDrivE', latLongs, r)
}

#' Calculate Geodesic Distance - Vincenty Ellipsoid Method
#'
#' This function calculates geodesic distance using the original Vincenty Ellipsoid method.
#'
#' @param latLongs Two column matrix of latitudes/longitudes
#' @param a Equatorial radius of the earth, default is WGS-84 radius
#' @param b Polar radius of the earth, default is WGS-84 radius
#' @param f Flattening or inverse eccentricity, default eccentricity is WGS-84
#' @param eps Convergence criteria
#' @param iter Maximum number of iterations to attempt convergence
#'
#' @examples
#' # two-column matrix with latitude/longitude, in degrees
#' latLong = cbind(runif(n = 5, min = 0, max = 90),
#'                 runif(n = 5, min = 0, max = 180))
#'
#' # Vincenty Ellipsoid  distance formula
#' distMat = calcVinEll(latLongs = latLong)
#'
#' @export
calcVinEll <- function(latLongs, a = 6378137, b = 6356752.3142, f = 1.0/298.257223563, eps = 1e-12, iter = 100) {
    .Call('_MGDrivE_calcVinEll', PACKAGE = 'MGDrivE', latLongs, a, b, f, eps, iter)
}

#' Calculate Lognormal Stochastic Matrix
#'
#' Given a distance matrix from \code{\link[MGDrivE]{calcVinEll}},
#' calculate a stochastic matrix where one step movement probabilities follow a lognormal density.
#'
#' The distribution and density functions for the lognormal kernel are given below:
#' \deqn{
#' F(x)=\frac{1}{2} + \frac{1}{2} \mathrm{erf}[\frac{\mathrm{ln}x-\mu}{\sqrt{2}\sigma}]
#' }
#' \deqn{
#' f(x)=\frac{1}{x\sigma\sqrt{2\pi}}\mathrm{exp}\left( -\frac{(\mathrm{ln}x-\mu)^{2}}{2\sigma^{2}} \right)
#' }
#' where \eqn{\mu} is the mean on the log scale, and \eqn{\sigma} is the standard deviation on the log scale.
#'
#' @param distMat Distance matrix from \code{\link[MGDrivE]{calcVinEll}}
#' @param meanlog Log mean of \code{\link[stats]{Lognormal}} distribution
#' @param sdlog Log standard deviation of \code{\link[stats]{Lognormal}} distribution
#'
#' @examples
#' # setup distance matrix
#' # two-column matrix with latitude/longitude, in degrees
#' latLong = cbind(runif(n = 5, min = 0, max = 90),
#'                 runif(n = 5, min = 0, max = 180))
#'
#' # Vincenty Ellipsoid  distance formula
#' distMat = calcVinEll(latLongs = latLong)
#'
#' # calculate lognormal distribution over distances
#' #  mean and standard deviation are just for example
#' kernMat = calcLognormalKernel(distMat = distMat, meanlog = 100, sdlog = 10)
#'
#' @export
calcLognormalKernel <- function(distMat, meanlog, sdlog) {
    .Call('_MGDrivE_calcLognormalKernel', PACKAGE = 'MGDrivE', distMat, meanlog, sdlog)
}

#' Calculate Gamma Stochastic Matrix
#'
#' Given a distance matrix from \code{\link[MGDrivE]{calcVinEll}}, calculate a
#' stochastic matrix where one step movement probabilities follow a gamma density.
#'
#' The distribution and density functions for the gamma kernel are given below:
#' \deqn{
#' F(x)=\frac{1}{\Gamma(\alpha)}\gamma(\alpha,\beta x)
#' }
#' \deqn{
#' f(x)=\frac{\beta^{\alpha}}{\Gamma(\alpha)}x^{\alpha-1}e^{-\beta x}
#' }
#' where \eqn{\Gamma(\alpha)} is the Gamma function, \eqn{\gamma(\alpha,\beta x)} is the lower incomplete
#' gamma function, and \eqn{\alpha,\beta} are the shape and rate parameters, respectively.
#'
#' @param distMat Distance matrix from \code{\link[MGDrivE]{calcVinEll}}
#' @param shape Shape parameter of \code{\link[stats]{GammaDist}} distribution
#' @param rate Rate parameter of \code{\link[stats]{GammaDist}} distribution
#'
#' @examples
#' # setup distance matrix
#' # two-column matrix with latitude/longitude, in degrees
#' latLong = cbind(runif(n = 5, min = 0, max = 90),
#'                 runif(n = 5, min = 0, max = 180))
#'
#' # Vincenty Ellipsoid  distance formula
#' distMat = calcVinEll(latLongs = latLong)
#'
#' # calculate gamma distribution over distances
#' #  shape and rate are just for example
#' kernMat = calcGammaKernel(distMat = distMat, shape = 1, rate = 1)
#'
#' @export
calcGammaKernel <- function(distMat, shape, rate) {
    .Call('_MGDrivE_calcGammaKernel', PACKAGE = 'MGDrivE', distMat, shape, rate)
}

#' Calculate Exponential Stochastic Matrix
#'
#' Given a distance matrix from \code{\link[MGDrivE]{calcVinEll}}, calculate a
#' stochastic matrix where one step movement probabilities follow an exponential density.
#'
#' The distribution and density functions for the exponential kernel are given below:
#' \deqn{
#' F(x)=1-e^{-\lambda x}
#' }
#' \deqn{
#' f(x)=\lambda e^{-\lambda x}
#' }
#' where \eqn{\lambda} is the rate parameter of the exponential distribution.
#'
#' @param distMat Distance matrix from \code{\link[MGDrivE]{calcVinEll}}
#' @param rate Rate parameter of \code{\link[stats]{Exponential}} distribution
#'
#' @examples
#' # setup distance matrix
#' # two-column matrix with latitude/longitude, in degrees
#' latLong = cbind(runif(n = 5, min = 0, max = 90),
#'                 runif(n = 5, min = 0, max = 180))
#'
#' # Vincenty Ellipsoid  distance formula
#' distMat = calcVinEll(latLongs = latLong)
#'
#' # calculate exponential distribution over distances
#' #  rate is just for example
#' kernMat = calcExpKernel(distMat = distMat, rate = 10)
#'
#' @export
calcExpKernel <- function(distMat, rate) {
    .Call('_MGDrivE_calcExpKernel', PACKAGE = 'MGDrivE', distMat, rate)
}

#' Calculate Zero-inflated Exponential Stochastic Matrix
#'
#' Given a distance matrix from \code{\link[MGDrivE]{calcVinEll}}, calculate a
#' stochastic matrix where one step movement probabilities follow an zero-inflated
#' exponential density with a point mass at zero. The point mass at zero represents
#' the first stage of a two-stage process, where mosquitoes decide to stay at
#' their current node or leave anywhere. This parameter can be calculated from
#' lifetime probabilities to stay at the current node with the helper function
#' \code{\link[MGDrivE]{calcZeroInflation}}.
#'
#' If a mosquito leaves its current node, with probability \eqn{1-p_{0}}, it
#' then chooses a destination node according to a standard exponential density
#' with rate parameter \eqn{rate}.
#'
#' The distribution and density functions for the zero inflated exponential kernel are given below:
#' \deqn{
#' F(x)=p_{0}\theta(x) + (1-p_{0})(1-e^{-\lambda x})
#' }
#' \deqn{
#' f(x)=p_{0}\delta(x)+(1-p_{0})\lambda e^{-\lambda x}
#' }
#' where \eqn{\lambda} is the rate parameter of the exponential distribution,
#' \eqn{\theta(x)} is the Heaviside step function and \eqn{\delta(x)} is the
#' Dirac delta function.
#'
#' @param distMat Distance matrix from \code{\link[MGDrivE]{calcVinEll}}
#' @param rate Rate parameter of \code{\link[stats]{Exponential}} distribution
#' @param p0 Point mass at zero
#'
#' @examples
#' # setup distance matrix
#' # two-column matrix with latitude/longitude, in degrees
#' latLong = cbind(runif(n = 5, min = 0, max = 90),
#'                 runif(n = 5, min = 0, max = 180))
#'
#' # Vincenty Ellipsoid  distance formula
#' distMat = calcVinEll(latLongs = latLong)
#'
#' # calculate hurdle exponential distribution over distances
#' #  rate and point mass are just for example
#' kernMat = calcHurdleExpKernel(distMat = distMat, rate = 1/1e6, p0 = 0.1)
#'
#' @export
calcHurdleExpKernel <- function(distMat, rate, p0) {
    .Call('_MGDrivE_calcHurdleExpKernel', PACKAGE = 'MGDrivE', distMat, rate, p0)
}