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# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' Calculate the "Bayesian Spatial Scan Statistic" by Neill et al. (2006).
#'
#' Calculate the "Bayesian Spatial Scan Statistic" by Neill et al. (2006),
#' adapted to a spatio-temporal setting. The scan statistic assumes that,
#' given the relative risk, the data follows a Poisson distribution. The
#' relative risk is in turn assigned a Gamma distribution prior, yielding a
#' negative binomial marginal distribution for the counts.
#' @param counts An integer matrix (most recent timepoint in first row).
#' @param baselines A matrix with positive entries (most recent timepoint in
#' first row).
#' @param zones An integer vector (all zones concatenated; locations indexed
#' from 0 and up).
#' @param zone_lengths An integer vector.
#' @param outbreak_prob A scalar; the probability of an outbreak (at any time,
#' any place).
#' @param alpha_null A scalar; the shape parameter for the gamma distribution
#' under the null hypothesis of no anomaly.
#' @param beta_null A scalar; the scale parameter for the gamma distribution
#' under the null hypothesis of no anomaly.
#' @param alpha_alt A scalar; the shape parameter for the gamma distribution
#' under the alternative hypothesis of an anomaly.
#' @param beta_alt A scalar; the scale parameter for the gamma distribution
#' under the alternative hypothesis of an anomaly.
#' @param inc_values A vector of possible values for the increase in the mean
#' (and variance) of an anomalous count.
#' @param inc_probs A vector of the prior probabilities of each value in
#' \code{inc_values}.
#' @return A list with elements \code{priors} (list), \code{posteriors} (list),
#' and \code{marginal_data_prob} (scalar). The list \code{priors} has
#' elements
#' \describe{
#' \item{null_prior}{The prior probability of no anomaly.}
#' \item{alt_prior}{The prior probability of an anomaly.}
#' \item{inc_prior}{A vector (matrix with 1 row) of prior probabilities
#' of each value in the argument \code{m_values}.}
#' \item{window_prior}{The prior probability of an outbreak in any of the
#' space-time windows.}
#' }
#' The list \code{posteriors} has elements
#' \describe{
#' \item{null_posterior}{The posterior probability of no anomaly.}
#' \item{alt_posterior}{The posterior probability of an anomaly.}
#' \item{inc_posterior}{A data frame with columns \code{inc_values} and
#' \code{inc_posterior}.}
#' \item{window_posteriors}{A data frame with columns \code{zone},
#' \code{duration}, \code{log_posterior} and
#' \code{log_bayes_factor}, each row
#' corresponding to a space-time window.}
#' \item{space_time_posteriors}{A matrix with the posterior anomaly
#' probability of each location-time
#' combination.}
#' \item{location_posteriors}{A vector (matrix with 1 row) with the
#' posterior probability of an anomaly at each
#' location.}
#' }
#' @export
#' @keywords internal
scan_bayes_negbin_cpp <- function(counts, baselines, zones, zone_lengths, outbreak_prob, alpha_null, beta_null, alpha_alt, beta_alt, inc_values, inc_probs) {
.Call(`_scanstatistics_scan_bayes_negbin_cpp`, counts, baselines, zones, zone_lengths, outbreak_prob, alpha_null, beta_null, alpha_alt, beta_alt, inc_values, inc_probs)
}
#' Calculate the expectation-based negative binomial scan statistic.
#'
#' Calculate the expectation-based negative binomial scan statistic and Monte
#' Carlo replicates.
#' @param counts Integer matrix (most recent timepoint in first row)
#' @param baselines Matrix (most recent timepoint in first row)
#' @param overdisp Matrix (most recent timepoint in first row)
#' @param zones Integer vector (all zones concatenated; locations indexed from
#' 0 and up)
#' @param zone_lengths Integer vector
#' @param store_everything Boolean
#' @param num_mcsim Integer
#' @param score_hotspot Boolean
#' @return A list with elements \code{observed} and \code{simulated}, each
#' being a data frame with columns:
#' \describe{
#' \item{zone}{The top-scoring zone (spatial component of MLC).}
#' \item{duration}{The corresponding duration (time-length of MLC).}
#' \item{score}{The value of the loglihood ratio statistic (the scan
#' statistic).}
#' \item{relrisk}{The estimated relative risk.}
#' \item{n_iter}{The number of iterations performed by the EM algorithm.}
#' }
#' @export
#' @keywords internal
scan_eb_negbin_cpp <- function(counts, baselines, overdisp, zones, zone_lengths, store_everything, num_mcsim, score_hotspot) {
.Call(`_scanstatistics_scan_eb_negbin_cpp`, counts, baselines, overdisp, zones, zone_lengths, store_everything, num_mcsim, score_hotspot)
}
#' Calculate the expecation-based Poisson scan statistic.
#'
#' Calculate the expectation-based Poisson scan statistic and Monte Carlo
#' replicates.
#' @param counts An integer matrix (most recent timepoint in first row).
#' @param baselines A matrix with positive entries (most recent timepoint in
#' first row).
#' @param zones An integer vector (all zones concatenated; locations indexed
#' from 0 and up).
#' @param zone_lengths An integer vector.
#' @param store_everything A boolean.
#' @param num_mcsim An integer.
#' @return A list with elements \code{observed} and \code{simulated}, each
#' being a data frame with columns:
#' \describe{
#' \item{zone}{The top-scoring zone (spatial component of MLC).}
#' \item{duration}{The corresponding duration (time-length of MLC).}
#' \item{score}{The value of the loglihood ratio statistic (the scan
#' statistic).}
#' \item{relrisk_in}{The estimated relative risk inside.}
#' \item{relrisk_in}{The estimated relative risk outside.}
#' }
#' @export
#' @keywords internal
scan_eb_poisson_cpp <- function(counts, baselines, zones, zone_lengths, store_everything, num_mcsim) {
.Call(`_scanstatistics_scan_eb_poisson_cpp`, counts, baselines, zones, zone_lengths, store_everything, num_mcsim)
}
#' Calculate the highest-value EB ZIP loglihood ratio statistic.
#'
#' Calculate the expectation-based ZIP loglihood ratio statistic for each zone
#' and duration, but only keep the zone and duration with the highest value
#' (the MLC). The estimate of the relative risk is also calculated, along with
#' the number of iterations the EM algorithm performed.
#' @param counts matrix (most recent timepoint in first row)
#' @param baselines matrix (most recent timepoint in first row)
#' @param probs matrix (most recent timepoint in first row)
#' @param zones integer vector (all zones concatenated; locations indexed from
#' 0 and up)
#' @param zone_lengths integer vector
#' @param rel_tol double
#' @param store_everything boolean
#' @param num_mcsim int
#' @return A list with elements \code{observed} and \code{simulated}, each
#' being a data frame with columns:
#' \describe{
#' \item{zone}{The top-scoring zone (spatial component of MLC).}
#' \item{duration}{The corresponding duration (time-length of MLC).}
#' \item{score}{The value of the loglihood ratio statistic (the scan
#' statistic).}
#' \item{relrisk}{The estimated relative risk.}
#' \item{n_iter}{The number of iterations performed by the EM algorithm.}
#' }
#' @export
#' @keywords internal
scan_eb_zip_cpp <- function(counts, baselines, probs, zones, zone_lengths, rel_tol, store_everything, num_mcsim) {
.Call(`_scanstatistics_scan_eb_zip_cpp`, counts, baselines, probs, zones, zone_lengths, rel_tol, store_everything, num_mcsim)
}
#' Calculate the space-time permutation scan statistic.
#'
#' Calculate the space-time permutation scan statistic (Kulldorff 2005) and
#' Monte Carloo replicates.
#' @param counts An integer matrix (most recent timepoint in first row).
#' @param baselines A matrix with positive entries (most recent timepoint in
#' first row).
#' @param zones An integer vector (all zones concatenated; locations indexed
#' from 0 and up)
#' @param zone_lengths An integer vector.
#' @param store_everything A boolean.
#' @param num_mcsim An integer.
#' @return A list with elements \code{observed} and \code{simulated}, each
#' being a data frame with columns:
#' \describe{
#' \item{zone}{The top-scoring zone (spatial component of MLC).}
#' \item{duration}{The corresponding duration (time-length of MLC).}
#' \item{score}{The value of the loglihood ratio statistic (the scan
#' statistic).}
#' \item{relrisk_in}{The estimated relative risk inside.}
#' \item{relrisk_in}{The estimated relative risk outside.}
#' }
#' @export
#' @keywords internal
scan_pb_perm_cpp <- function(counts, baselines, zones, zone_lengths, store_everything, num_mcsim) {
.Call(`_scanstatistics_scan_pb_perm_cpp`, counts, baselines, zones, zone_lengths, store_everything, num_mcsim)
}
#' Calculate the population-based Poisson scan statistic.
#'
#' Calculate the population-based Poisson scan statistic and Monte Carlo
#' replicates.
#' @param counts integer matrix (most recent timepoint in first row)
#' @param baselines matrix (most recent timepoint in first row)
#' @param zones integer vector (all zones concatenated; locations indexed from
#' 0 and up)
#' @param zone_lengths integer vector
#' @param store_everything boolean
#' @param num_mcsim int
#' @return A list with elements \code{observed} and \code{simulated}, each
#' being a data frame with columns:
#' \describe{
#' \item{zone}{The top-scoring zone (spatial component of MLC).}
#' \item{duration}{The corresponding duration (time-length of MLC).}
#' \item{score}{The value of the loglihood ratio statistic (the scan
#' statistic).}
#' \item{relrisk_in}{The estimated relative risk inside.}
#' \item{relrisk_in}{The estimated relative risk outside.}
#' }
#' @export
#' @keywords internal
scan_pb_poisson_cpp <- function(counts, baselines, zones, zone_lengths, store_everything, num_mcsim) {
.Call(`_scanstatistics_scan_pb_poisson_cpp`, counts, baselines, zones, zone_lengths, store_everything, num_mcsim)
}
#' Get indices of zero elements in a vector.
#' @param v An integer vector.
#' @return A vector with the indices of elements equal to zero in \code{v}.
#' Indices start at zero.
#' @keywords internal
#' @export
get_zero_indices <- function(v) {
.Call(`_scanstatistics_get_zero_indices`, v)
}
#' Permute the entries of the matrix, preserving row and column marginals.
#'
#' Permute the entries of the matrix, preserving row and column marginals.
#' @param A An integer matrix.
#' @return An integer matrix.
#' @keywords internal
permute_matrix <- function(A) {
.Call(`_scanstatistics_permute_matrix`, A)
}
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