R/RcppExports.R

In isabelfulcher/autognet: Estimation of causal network effects

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

oneMultinomCall <- function(probs) {
    .Call('_autognet_oneMultinomCall', PACKAGE = 'autognet', probs)
}

oneMultinomC <- function(probs) {
    .Call('_autognet_oneMultinomC', PACKAGE = 'autognet', probs)
}

callRMultinom <- function(x) {
    .Call('_autognet_callRMultinom', PACKAGE = 'autognet', x)
}

#' Run Gibbs sampler for auxiliary covariate values using Rcpp
#'
#' Given the specific inputs, determine auxiliary covariate
#' values using a Gibbs sampling procedure.
#'
#' @param tau A numeric vector for the intercept terms in the covariate model
#' @param rho A numeric vector for the correlation terms in the covariate model
#' @param nu A numberic matrix for the neighbor terms in the covariate model
#' @param N An integer indicating the size of the interconnected network
#' @param R An integer indicating the number of iterations for the Gibbs
#' @param J An integer for the number of covariates
#' @param rho_mat A numeric matrix for rho terms
#' @param adjacency A binary matrix indicating connected units
#' @param cov_i A numeric matrix for observed covariate values (starting values for chain)
#' @param weights A numeric vector indicating the number of neighbors for each node
#' @param group_lengths An integer vector indicating the number of categories for each variable
#' @param group_functions An integer vector indicating the type of variable
#' @param additional_nu An integer (0/1) specifying whether neighbor cross terms will be evaluated (i.e. non zero)
#' @return A numeric matrix for auxiliary covariate values
#' between [0,1]
#'
#'
#' @export
auxVarCpp <- function(tau, rho, nu, N, R, J, rho_mat, adjacency, cov_i, weights, group_lengths, group_functions, additional_nu) {
    .Call('_autognet_auxVarCpp', PACKAGE = 'autognet', tau, rho, nu, N, R, J, rho_mat, adjacency, cov_i, weights, group_lengths, group_functions, additional_nu)
}

#' Run Gibbs sampler for auxiliary outcome values using Rcpp
#'
#' Given the specific inputs, determine auxiliary outcome
#' values using a Gibbs sampling procedure.
#'
#' @param beta A numeric vector of parameters from outcome model
#' @param trt A numeric vector of the treatment values
#' @param cov A numeric matrix for observed covariate values (starting values for chain)
#' @param N An integer indicating the size of the interconnected network
#' @param R An integer indicating the number of iterations for the Gibbs
#' @param adjacency A binary matrix indicating connected units
#' @param start A vector of the initializing values of
#' @param weights A numeric vector indicating the number of neighbors for each node
#' @return A numeric vector for auxiliary covariate outcomes
#' as an element of {0,1}
#'
#'
#' @export
auxVarOutcomeCpp <- function(beta, trt, cov, N, R, adjacency, start, weights) {
    .Call('_autognet_auxVarOutcomeCpp', PACKAGE = 'autognet', beta, trt, cov, N, R, adjacency, start, weights)
}

#' Run Gibbs sampler for covariates to use in network causal effect estimation
#'
#' Given the specific inputs, determine covariate
#' values using a Gibbs sampling procedure.
#'
#' @param tau A numeric vector for the intercept terms in the covariate model
#' @param rho A numeric vector for the correlation terms in the covariate model
#' @param nu A numberic matrix for the neighbor terms in the covariate model
#' @param ncov An integer for the number of covariates
#' @param R An integer indicating the number of iterations for the Gibbs
#' @param N An integer indicating the size of the interconnected network
#' @param burnin An integer indicating when to start saving values in the chain
#' @param rho_mat A numeric matrix for rho terms
#' @param adjacency A binary matrix indicating connected units
#' @param weights A numeric vector indicating the number of neighbors for each node
#' @param cov_mat A numeric matrix for starting values for each covariate
#' @param group_lengths An integer vector indicating the number of categories for each variable
#' @param group_functions An integer vector indicating the type of variable
#' @param additional_nu An integer (0/1) specifying whether neighbor cross terms will be evaluated (i.e. non-zero)
#'
#' @return A list of numeric matrices that contain the covariate values and neighbor covariate
#' values for each person at that specific point in the chain
#'
#'
#' @export
networkGibbsOutCovCpp <- function(tau, rho, nu, ncov, R, N, burnin, rho_mat, adjacency, weights, cov_mat, group_lengths, group_functions, additional_nu) {
    .Call('_autognet_networkGibbsOutCovCpp', PACKAGE = 'autognet', tau, rho, nu, ncov, R, N, burnin, rho_mat, adjacency, weights, cov_mat, group_lengths, group_functions, additional_nu)
}

#' Estimate overall network causal effect
#'
#' Given the specific inputs, determine estimates of psi (overall) for each
#' unit under some treatment regime using a Gibbs sampling procedure.
#'
#' @param cov_list The output from networkGibbsOutCovCpp function
#' @param beta A numeric vector for the parameters from the outcome model
#' @param p A probability of treated units for the binomial treatment assignment draw
#' @param ncov A numeric vector for the parameters from the outcome model
#' @param R An integer indicating the number of iterations for the Gibbs
#' @param N An integer indicating the size of the interconnected network
#' @param adjacency A binary matrix indicating connected units
#' @param weights A numeric vector indicating the number of neighbors for each node
#' @param burnin The index to start evaluation as one would normally have for a burnin
#' for a Bayesian computation.
#' @param average An indicator of whether to evaluate the causal effects as an average
#' of the R iterations
#' @return A vector of length N containing the estimated value of psi for each person
#'
#' @export
networkGibbsOuts1Cpp <- function(cov_list, beta, p, ncov, R, N, adjacency, weights, burnin, average) {
    .Call('_autognet_networkGibbsOuts1Cpp', PACKAGE = 'autognet', cov_list, beta, p, ncov, R, N, adjacency, weights, burnin, average)
}

#' Estimate components for direct and spillover effects
#'
#' Given the specific inputs, determine estimates of psi for the given
#' units under some treatment regime and individual treatment value
#' using a Gibbs sampling procedure.
#'
#' @param cov_list The output from networkGibbsOutCovCpp function
#' @param beta A numeric vector for the parameters from the outcome model
#' @param p A probability of treated units for the binomial treatment assignment draw
#' @param ncov A numeric vector for the parameters from the outcome model
#' @param R An integer indicating the number of iterations for the Gibbs
#' @param N An integer indicating the size of the interconnected network
#' @param adjacency A binary matrix indicating connected units
#' @param weights A numeric vector indicating the number of neighbors for each node
#' @param subset The indices of the individuals, as they appear in the adjacency matrix,
#' to be included in the network causal effects estimates.
#' @param treatment_value The intervened value of an individual's treatment assignment
#' for each person in subset
#' @param burnin The index to start evaluation as one would normally have for a burnin
#' for a Bayesian computation.
#' @param average An indicator of whether to evaluate the causal effects as an average
#' of the R iterations
#' @return A vector of length N containing the estimated value of psi for each person
#'
#'
#' @export
networkGibbsOuts2Cpp <- function(cov_list, beta, p, ncov, R, N, adjacency, weights, subset, treatment_value, burnin, average) {
    .Call('_autognet_networkGibbsOuts2Cpp', PACKAGE = 'autognet', cov_list, beta, p, ncov, R, N, adjacency, weights, subset, treatment_value, burnin, average)
}