R/right_truncated_distribution.R

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

#' @title Samples from a right-truncated distribution.
#' @description
#' A right-truncated distribution is a statistical distribution that arises
#' when the possible values  of a random variable are restricted to be below a
#' certain specified value `high`. In essence, the right tail of the original distribution is "cut off"
#' at a particular point, and the remaining probability is redistributed among the allowable values.
#' This type of distribution is common in various fields where there are inherent upper limits or observational constraints.
#' @param base_dist The base distribution to truncate. Must be a univariate distribution with real support.
#' @param high (float, jnp.ndarray, optional): The upper truncation point. The support of the new distribution is \eqn{(-\infty, \text{high})}. Defaults to 0.0.
#' @param shape A numeric vector. When \code{sample=FALSE} (model building), this is used with `.expand(shape)` to set the distribution's batch shape. When \code{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 to mask observations.
#' @param create_obj Logical. If `TRUE`, returns the raw BI distribution object instead of creating a sample site.
#' @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 Boolean. Indicates whether to return a JAX array or not.
#'
#' @return
#'  - When \code{sample=FALSE}, a BI right-truncated distribution object (for model building).
#'
#'  - When \code{sample=TRUE}, a JAX array of samples drawn from the right-truncated distribution (for direct sampling).
#'
#'  - When \code{create_obj=TRUE}, the raw BI distribution object (for advanced use cases).
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m=importBF(platform='cpu')
#' bf.dist.right_truncated_distribution(
#' base_dist = bf.dist.normal(0,1, create_obj = TRUE),
#' high = 10,
#' sample = TRUE)
#' }
#' @export
bf.dist.right_truncated_distribution=function(base_dist, high=0.0, 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$right_truncated_distribution(
       base_dist,
       high = .BF_env$jnp$array(high),
       validate_args= validate_args,  name= name,  obs= obs,  mask= mask,  sample= sample,  seed= seed,  shape= shape,  event= event,  create_obj= create_obj,   to_jax = to_jax)
}