R/zero_inflated_poisson.R

In BayesForge: Bayesian Inference using 'numpyro' and 'XLA'

#' @title A Zero Inflated Poisson distribution.
#' @description
#' The Zero-Inflated Poisson distribution is a discrete count-distribution designed for data with *more zeros*
#' than would be expected under a standard Poisson. Essentially, it assumes two underlying processes:

#' * With probability \deqn{\pi} you are in a "structural zero" state (i.e., you automatically get a zero count).
#' * With probability \deqn{1 - \pi} you draw from a standard Poisson distribution with parameter \deqn{\lambda}.
#'
#' This results in a mixture distribution that places more mass at zero than a Poisson alone would.
#' It's widely used in, for instance, ecology (species counts with many zeros), insurance/claims problems, and any count-data setting with excess zeros.
#'
#' @param gate The gate parameter.
#' @param rate A numeric vector, matrix, or array representing the rate parameter of the underlying Poisson distribution.
#' @param shape A numeric vector used to shape the distribution. When `sample=False` (model building), this is used with `.expand(shape)` to set the distribution's batch shape. When `sample=True` (direct sampling), this is used as `sample_shape` to draw a raw JAX array of the given shape.
#' @param event The number of batch dimensions to reinterpret as event dimensions (used in model building).
#' @param mask An optional boolean vector, matrix, or array to mask observations.
#' @param create_obj Logical; If `TRUE`, returns the raw BI distribution object instead of creating a sample site.
#' @param sample Logical; If `TRUE`, draws samples from the distribution.
#' @param validate_args Logical: Whether to validate parameter values.  Defaults to `reticulate::py_none()`.
#' @param sample A logical value that controls the function's behavior. If `TRUE`,
#'   the function will directly draw samples from the distribution. If `FALSE`,
#'   it will create a random variable within a model. Defaults to `FALSE`.
#' @param seed An integer used to set the random seed for reproducibility when
#'   `sample = TRUE`. This argument has no effect when `sample = FALSE`, as
#'   randomness is handled by the model's inference engine. Defaults to 0.
#' @param obs A numeric vector or array of observed values. If provided, the
#'   random variable is conditioned on these values. If `NULL`, the variable is
#'   treated as a latent (unobserved) variable. Defaults to `NULL`.
#' @param name A character string representing the name of the random variable
#'   within a model. This is used to uniquely identify the variable. Defaults to 'x'.
#' @param to_jax Logical. Defaults to TRUE.
#'
#' @return
#' \itemize{
#'   \item When \code{sample=FALSE}, a BI Zero Inflated Poisson distribution object (for model building).
#'   \item When \code{sample=TRUE}, a JAX array of samples drawn from the Zero Inflated Poisson distribution (for direct sampling).
#'   \item When \code{create_obj=TRUE}, the raw BI distribution object (for advanced use cases).
#' }
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m <- importBF(platform = "cpu")
#' bf.dist.zero_inflated_poisson(gate = 0.3, rate = 5, sample = TRUE)
#' }
#' @export
bf.dist.zero_inflated_poisson <- function(gate, rate = 1.0, validate_args = py_none(), name = "x", obs = py_none(), mask = py_none(), sample = FALSE, seed = py_none(), shape = c(), event = 0, create_obj = FALSE, to_jax = TRUE) {
  shape <- do.call(tuple, as.list(as.integer(shape)))
  if (!.BF_env$.py$is_none(seed)) {
    seed <- as.integer(seed)
  }
  .BF_env$.bf_instance$dist$zero_inflated_poisson(
    gate = .BF_env$jnp$array(gate),
    rate = .BF_env$jnp$array(rate),
    validate_args = validate_args, name = name, obs = obs, mask = mask, sample = sample, seed = seed, shape = shape, event = event, create_obj = create_obj
  )
}