R/RcppExports.R

In vnminin/bayessir: Particle marginal Metropolis-Hastings (PMMH) algorithm for time-varying Susceptible-Infectious-Recovered-Susceptible (SIRS) model

# This file was generated by Rcpp::compileAttributes
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' Sample from initial distributions
#' 
#' Simulates the initial number of susceptible and infected individuals from independent Poisson initial distributions.  
#' @param n Number of particles 
#' @param startState Numeric vector of size two containing the means of initial distributions for the numbers of susceptible and infected individuals respectively
#' @return Matrix of dimension n by 2, where each row is the initial state for a particle trajectory
#' @export
simInit <- function(n, startState) {
    .Call('bayessir_simInit', PACKAGE = 'bayessir', n, startState)
}

#' Sample from initial distributions for SIWR
#' 
#' Simulates the initial number of susceptible and infected individuals from independent Poisson initial distributions.  
#' @param n Number of particles 
#' @param startState Numeric vector of size two containing the means of initial distributions for the numbers of susceptible and infected individuals respectively
#' @return Matrix of dimension n by 2, where each row is the initial state for a particle trajectory
#' @export
simInitSIWR <- function(n, startState) {
    .Call('bayessir_simInitSIWR', PACKAGE = 'bayessir', n, startState)
}

#' Probability of observed data
#' 
#' Calculates the probability of the observed data given the latent states and the parameters, assuming the number of observed infections has a binomial distribution 
#' @param infected Number of infected individuals
#' @param observed Number of observed individuals
#' @param repRate probability of infected individuals seeking treatment
#' @param givelog boolian; if 1, returns log 
#' @return Probability of observed data 
#' @export
dataLik <- function(infected, observed, repRate, givelog) {
    .Call('bayessir_dataLik', PACKAGE = 'bayessir', infected, observed, repRate, givelog)
}

#' Samples a vector based on weights
#' @param weights input numeric vector
#' @return Index of vector selected
#' @export
sampleOnce <- function(weights) {
    .Call('bayessir_sampleOnce', PACKAGE = 'bayessir', weights)
}

SIRSGillespie <- function(startState, popSize, startTime, intervalLength, parameters, alphas) {
    .Call('bayessir_SIRSGillespie', PACKAGE = 'bayessir', startState, popSize, startTime, intervalLength, parameters, alphas)
}

#' Modified inhomogeneous Gillespie algorithm 
#' 
#' Uses a modified Gillespie algorithm to simulate the numbers of susceptible and infected individuals forward in time when value of environmental force of infection changes
#' @param startState Integer vector of size two containing the initial number of susceptible and infected individuals respectively
#' @param popSize Population size
#' @param startTime Start time of simulation
#' @param intervalLength Length of simulation interval 
#' @param parameters Numeric vector of size seven containing the current values of the infectious contact rate, the recovery rate, the rate at which immunity is lost, and the powers
#' @param alphas Numeric vector containing the values of alpha
#' @param allbreaks Numeric vector containing the times at which the value of alpha changes
#' @return Vector containing number of susceptible and infected at the end of the simulation interval 
#' @export
inhomoSIRSGillespie <- function(startState, popSize, startTime, intervalLength, parameters, alphas, allbreaks) {
    .Call('bayessir_inhomoSIRSGillespie', PACKAGE = 'bayessir', startState, popSize, startTime, intervalLength, parameters, alphas, allbreaks)
}

SIWRGillespie <- function(startState, popSize, Wmax, startTime, intervalLength, parameters, alphas) {
    .Call('bayessir_SIWRGillespie', PACKAGE = 'bayessir', startState, popSize, Wmax, startTime, intervalLength, parameters, alphas)
}

inhomoSIWRSGillespie <- function(startState, popSize, Wmax, startTime, intervalLength, parameters, alphas, allbreaks) {
    .Call('bayessir_inhomoSIWRSGillespie', PACKAGE = 'bayessir', startState, popSize, Wmax, startTime, intervalLength, parameters, alphas, allbreaks)
}

#' Modified inhomogeneous Poisson tau-leaping algorithm 
#' 
#' Uses a tau-leaping algorithm to simulate the numbers of susceptible and infected individuals forward in time when value of environmental force of infection changes
#' @param startState Integer vector of size two containing the initial number of susceptible and infected individuals respectively
#' @param popSize Population size
#' @param startTime Start time of simulation
#' @param intervalLength Length of simulation interval 
#' @param parameters Numeric vector of size seven containing the current values of the infectious contact rate, the recovery rate, the rate at which immunity is lost, and the powers
#' @param alphas Numeric vector containing the values of alpha
#' @param allbreaks Numeric vector containing the times at which the value of alpha changes
#' @param deltatint Initial value to use for tau in tau-leaping algorithm
#' @param ncrit Critical size for modified tau-leaping algorithm; if the population of a compartment is lower than this number a single step algorithm is used until the population gets above the critical size.
#' @return Vector containing number of susceptible and infected at the end of the simulation interval 
#' @export
inhomoModPoissonTL <- function(startState, popSize, startTime, intervalLength, parameters, alphas, allbreaks, deltatint, ncrit) {
    .Call('bayessir_inhomoModPoissonTL', PACKAGE = 'bayessir', startState, popSize, startTime, intervalLength, parameters, alphas, allbreaks, deltatint, ncrit)
}

#' Modified homogeneous Poisson tau-leaping algorithm 
#' 
#' Modified Poisson tau-leaping algorithm. Uses a tau-leaping algorithm to simulate the numbers of susceptible and infected individuals forward in time when value of environmental force of infection remains constant
#' @param startState Integer vector of size two containing the initial number of susceptible and infected individuals respectively
#' @param popSize Population size
#' @param startTime Start time of simulation
#' @param intervalLength Length of simulation interval 
#' @param parameters parameters Numeric vector of size seven containing the current values of the infectious contact rate, the recovery rate, the rate at which immunity is lost, and the powers
#' @param alpha The value of the time-varying environmental force of infection
#' @param deltatint Initial value to use for tau in tau-leaping algorithm
#' @param ncrit Critical number
#' @return Vector containing number of susceptible and infected at the end of the simulation interval 
#' @export
ModPoissonTL <- function(startState, popSize, startTime, intervalLength, parameters, alpha, deltatint, ncrit) {
    .Call('bayessir_ModPoissonTL', PACKAGE = 'bayessir', startState, popSize, startTime, intervalLength, parameters, alpha, deltatint, ncrit)
}

#' Modified inhomogeneous Poisson tau-leaping algorithm for SIWR model
#' 
#' Uses a tau-leaping algorithm to simulate S, I, W, and R forward in time when value of environmental force of infection changes
#' @param startState Integer vector of size three containing the initial values for S, I, and W
#' @param popSize Population size
#' @param startTime Start time of simulation
#' @param intervalLength Length of simulation interval 
#' @param parameters Numeric vector of size six containing the current values of the infectious contact rate, betaW, the recovery rate, the rate at which immunity is lost, kappa, and eta
#' @param alphas Numeric vector containing the values of alpha
#' @param allbreaks Numeric vector containing the times at which the value of alpha changes
#' @param deltatint Initial value to use for tau in tau-leaping algorithm
#' @param ncrit Critical size for modified tau-leaping algorithm; if the population of a compartment is lower than this number a single step algorithm is used until the population gets above the critical size.
#' @return Vector containing values of S, I, and W at the end of the simulation interval 
#' @export
inhomoModPoissonTLforSIWR <- function(startState, popSize, Wmax, startTime, intervalLength, parameters, alphas, allbreaks, deltatint, ncrit) {
    .Call('bayessir_inhomoModPoissonTLforSIWR', PACKAGE = 'bayessir', startState, popSize, Wmax, startTime, intervalLength, parameters, alphas, allbreaks, deltatint, ncrit)
}

#' Modified homogeneous Poisson tau-leaping algorithm for SIWR model
#' 
#' Modified Poisson tau-leaping algorithm. Uses a tau-leaping algorithm to simulate S, I, W, and R forward in time when value of environmental force of infection remains constant
#' @param startState Integer vector of size three containing the initial values for S, I, and W
#' @param popSize Population size
#' @param startTime Start time of simulation
#' @param intervalLength Length of simulation interval 
#' @param parameters Numeric vector of size six containing the current values of the infectious contact rate, betaW, the recovery rate, the rate at which immunity is lost, kappa, and eta
#' @param alpha The value of the time-varying environmental force of infection
#' @param deltatint Initial value to use for tau in tau-leaping algorithm
#' @param ncrit Critical number
#' @return Vector containing values of S, I, and W at the end of the simulation interval 
#' @export
ModPoissonTLforSIWR <- function(startState, popSize, Wmax, startTime, intervalLength, parameters, alpha, deltatint, ncrit) {
    .Call('bayessir_ModPoissonTLforSIWR', PACKAGE = 'bayessir', startState, popSize, Wmax, startTime, intervalLength, parameters, alpha, deltatint, ncrit)
}

#' Sequential Monte Carlo algorithm
#' 
#' 
#' @param observedCounts Integer vector of observed counts
#' @param observedTimes Numeric vector of observation times
#' @param parameters Numeric vector of size seven containing the current values of the infectious contact rate, the recovery rate, the rate at which immunity is lost, and the powers
#' @param alphaBreaks List containing the time-varying environmental force of infection and the times at which the rate changes, broken up by simulation intervals
#' @param startMeans Numeric vector of size two containing the means of initial distributions for the numbers of susceptible and infected individuals respectively
#' @param nparticles Number of particles 
#' @param popSize Population size
#' @param reportingRate Probability of infected individuals seeking treatment
#' @param deltatval Initial value to use for tau in tau-leaping algorithm
#' @param ncrit Critical size for modified tau-leaping algorithm; if the population of a compartment is lower than this number a single step algorithm is used until the population gets above the critical size.
#' @param UseGill boolian; if 1, uses the gillespie algorithm. If 0, uses tau-leaping algorithm
#' @return log-likelihood and estimate of last state
#' @export
SMCwithModPossionTL <- function(observedCounts, observedTimes, parameters, alphaBreaks, startMeans, nparticles, popSize, reportingRate, deltatval, ncrit, UseGill, UseSIWR) {
    .Call('bayessir_SMCwithModPossionTL', PACKAGE = 'bayessir', observedCounts, observedTimes, parameters, alphaBreaks, startMeans, nparticles, popSize, reportingRate, deltatval, ncrit, UseGill, UseSIWR)
}

#' Sequential Monte Carlo algorithm for SIWR 
#' 
#' 
#' @param observedCounts Integer vector of observed counts
#' @param observedTimes Numeric vector of observation times
#' @param parameters Numeric vector of size seven containing the current values of the infectious contact rate, the recovery rate, the rate at which immunity is lost, and the powers
#' @param alphaBreaks List containing the time-varying environmental force of infection and the times at which the rate changes, broken up by simulation intervals
#' @param startMeans Numeric vector of size two containing the means of initial distributions for the numbers of susceptible and infected individuals respectively
#' @param nparticles Number of particles 
#' @param popSize Population size
#' @param reportingRate Probability of infected individuals seeking treatment
#' @param deltatval Initial value to use for tau in tau-leaping algorithm
#' @param ncrit Critical size for modified tau-leaping algorithm; if the population of a compartment is lower than this number a single step algorithm is used until the population gets above the critical size.
#' @param UseGill boolian; if 1, uses the gillespie algorithm. If 0, uses tau-leaping algorithm
#' @param maxWval Upper bound for W compartment. If 0, no bounding.
#' @return log-likelihood and estimate of last state
#' @export
SMCforSIWR <- function(observedCounts, observedTimes, parameters, alphaBreaks, startMeans, nparticles, popSize, reportingRate, deltatval, ncrit, UseGill, maxWval) {
    .Call('bayessir_SMCforSIWR', PACKAGE = 'bayessir', observedCounts, observedTimes, parameters, alphaBreaks, startMeans, nparticles, popSize, reportingRate, deltatval, ncrit, UseGill, maxWval)
}

postPredSampleSIRS <- function(observedTimes, parameters, alphaBreaks, startMeans, popSize, reportingRate) {
    .Call('bayessir_postPredSampleSIRS', PACKAGE = 'bayessir', observedTimes, parameters, alphaBreaks, startMeans, popSize, reportingRate)
}

postPredSampleSIRSwithTL <- function(observedTimes, parameters, alphaBreaks, startMeans, popSize, reportingRate, deltatval, ncrit) {
    .Call('bayessir_postPredSampleSIRSwithTL', PACKAGE = 'bayessir', observedTimes, parameters, alphaBreaks, startMeans, popSize, reportingRate, deltatval, ncrit)
}

postPredSampleSIWR <- function(observedTimes, parameters, alphaBreaks, startMeans, popSize, maxWval, reportingRate) {
    .Call('bayessir_postPredSampleSIWR', PACKAGE = 'bayessir', observedTimes, parameters, alphaBreaks, startMeans, popSize, maxWval, reportingRate)
}