R/logistic.R

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

#' @title Samples from a Logistic distribution.
#' @description
#' The Logistic distribution is a continuous probability distribution defined by two parameters: location and scale.
#' It is often used to model growth processes and is closely related to the normal distribution.
#' Its CDF is the logistic (sigmoid) function, which makes it appealing in modeling probabilities,
#' logistic regression, and various growth models.
#' It resembles the normal distribution in shape (bell-shaped, symmetric) but has **heavier tails**
#' (i.e. more probability in the extremes) and simpler closed-form expressions for the CDF.
#'
#' @param loc Numeric vector or single number. The location parameter, specifying the median of the distribution. Defaults to 0.0.
#' @param scale Numeric vector or single number. The scale parameter, which determines the spread of the distribution. Must be positive. Defaults to 1.0.
#' @param shape Numeric vector. A multi-purpose argument for shaping. 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 Integer. The number of batch dimensions to reinterpret as event dimensions (used in model building).
#' @param mask Logical vector. Optional boolean array to mask observations.
#' @param create_obj Logical. If True, returns the raw BI distribution object instead of creating a sample site. This is essential for building complex distributions like `MixtureSameFamily`.
#' @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 Logistic distribution object (for model building).
#'
#'  - When \code{sample=TRUE}, a JAX array of samples drawn from the Logistic 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.logistic(sample = TRUE)
#' }
#' @export
bf.dist.logistic <- function(loc = 0.0, scale = 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)))
  reticulate::py_run_string("def is_none(x): return x is None")
  if (!.BF_env$.py$is_none(seed)) {
    seed <- as.integer(seed)
  }
  .BF_env$.bf_instance$dist$logistic(
    loc = .BF_env$jnp$array(loc),
    scale = .BF_env$jnp$array(scale),
    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
  )
}