R/RcppExports.R

In antarctic-humpback-2019-assessment/HumpbackSIR: Use Sample-Importance-Resampling to Estimate Southern Humpback Abundance

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

#' GENERALISED LOGISTIC MODEL
#'
#' \code{GENERALIZED_LOGISTIC} returns the population projection using a
#' Pella-Tomlison population dynamics model: $$N_{t+1} =
#' N_{t}+N_{t}*r_{max}*\left[ 1 - \left( \frac{N_{t}}{K} \right) ^z \right] -
#' C_{t}$$ where $N$ is the population size at year $t$ or $t+1$, $r_{max}$ is
#' the maximum net recruitment rate, $K$ is the pre-exploitation population
#' size, $z$ is the parameter that determines the population size where
#' productivity is maximum. For example, a value of 2.39 corresponds to maximum
#' sustainable yield of $0.6K$ and is assumed by the IWC SC, and $C_t$ is the
#' harvest in numbers in year $t$. Population size can be in either numbers or
#' biomass, however, units will have to be the same as the units used for catch,
#' relative abundance, and absolute abundance.
#'
#' @param r_max The maximum net recruitment rate ($r_{max}$).
#' @param K Pre-exploitation population size in numbers or biomass (depending on
#'   input).
#' @param N1 The population size in numbers or biomass at year 1 (generally
#'   assumed to be K).
#' @param z The parameter that determines the population size where productivity
#'   is maximum (assumed to be 2.39 by the IWC SC).
#' @param start_yr The first year of the projection (assumed to be the first
#'   year in the catch series).
#' @param num_Yrs The number of projection years. Set as the last year in the
#'   catch or abundance series, whichever is most recent, minus the
#'   \code{start_yr}.
#' @param catches The time series of catch in numbers or biomass. Currently does
#'   not handle NAs and zeros will have to input a priori for years in which
#'   there were no catches.
#' @param MVP The minimum viable population size in numbers or biomass. Computed
#'   as 4 * \code{\link{num.haplotypes}} to compute minimum viable population
#'   (from Jackson et al., 2006 and IWC, 2007).
#'
#' @return A list of the minimum population size \code{\link{Min.Pop}}, year of the minimum population size \code{\link{Min.Yr}}, a indicator of wether the minimum population size is below the \code{\link{MVP}}, and the predicted population size \code{Pred.N}.
#'
#' @examples
#' num_Yrs  <-  10
#' start_yr  <-  1
#' r_max  <-  0.2
#' K  <-  1000
#' N1  <-  K
#' catches  <-  round(runif(10, min = 0, max = 150 ), 0 )
#' MVP  <-  0
#' GENERALIZED_LOGISTIC(r_max, K, N1, z, start_yr, num_Yrs, catches)
GENERALIZED_LOGISTIC <- function(r_max, K, N1, z, start_yr, num_Yrs, catches, MVP) {
    .Call(`_HumpbackSIR_GENERALIZED_LOGISTIC`, r_max, K, N1, z, start_yr, num_Yrs, catches, MVP)
}

#' Adjusted lognormal likelihood from Zerbini et al. 2011 (eq. 5)
#'
#' \code{dlnorm_zerb} returns the density for the adjusted log normal distribution whose logarith has mean equal to \code{meanlog} and standard deviation equal to \code{sdlog}:
#' $$f\left(x\right) = \frac{1}{\sigma * x} exp \left( \frac{ \left( ln\left( x \right) - \mu \right) ^ 2} {2 \sigma^2} \right)$$
#'
#' @param x vector of quantiles.
#' @param meanlog mean of the distribution on the log scale with a default value of 0.
#' @param sdlog standard deviation of the distribution on the log scale with a default value of 1.
#' @param log logical; if TRUE, probabilities p are given as log(p).
#'
#' @return A vector of densities.
dlnorm_zerb <- function(x, meanlog, sdlog, return_log = TRUE) {
    .Call(`_HumpbackSIR_dlnorm_zerb`, x, meanlog, sdlog, return_log)
}