R/gaussian_copula.R

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

#' @title Gaussian Copula Distribution
#'
#' @description
#' A distribution that links the `batch_shape[:-1]` of a marginal distribution with a multivariate Gaussian copula, odelling the correlation between the axes.
#' A copula is a multivariate distribution over the uniform distribution on [0, 1].
#' The Gaussian copula links the marginal distributions through a multivariate normal distribution.
#'
#' @param marginal_dist Distribution: Distribution whose last batch axis is to be coupled.
#' @param correlation_matrix array_like, optional: Correlation matrix of the coupling multivariate normal distribution. Defaults to `reticulate::py_none()`.
#' @param correlation_cholesky array_like, optional: Correlation Cholesky factor of the coupling multivariate normal distribution. Defaults to `reticulate::py_none()`.
#' @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 int: The number of batch dimensions to reinterpret as event dimensions (used in model building).
#' @param mask jnp.ndarray, bool, optional: Optional boolean array to mask observations. Defaults to `reticulate::py_none()`.
#' @param create_obj bool, optional: If `TRUE`, returns the raw BI distribution object instead of creating a sample site. This is essential for building complex distributions like `MixtureSameFamily`. Defaults to `FALSE`.
#' @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 Gaussian Copula distribution object (for model building).
#'
#'  - When \code{sample=TRUE}, a JAX array of samples drawn from the Gaussian Copula 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.gaussian_copula(
#'   marginal_dist = bf.dist.gamma(concentration = 1 ,  create_obj = TRUE) ,
#'   correlation_matrix =  matrix(c(1.0, 0.7, 0.7, 1.0),, nrow = 2, byrow = TRUE),
#'   sample = TRUE)
#' }
#' @export
bf.dist.gaussian_copula=function(marginal_dist, correlation_matrix=py_none(), correlation_cholesky=py_none(), 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) {

      message("No more available since jax > 0.06")
      return(invisible(NULL))

      shape=do.call(tuple, as.list(as.integer(shape)))
     if (!.BF_env$.py$is_none(seed)){seed=as.integer(seed);}
     if(!.BF_env$.py$is_none(correlation_cholesky)){correlation_cholesky = .BF_env$jnp$array(correlation_cholesky)}
     if(!.BF_env$.py$is_none(correlation_matrix)){correlation_matrix = .BF_env$jnp$array(correlation_matrix)}
     .BF_env$.bf_instance$dist$gaussian_copula(
       marginal_dist = marginal_dist,
       correlation_matrix = correlation_matrix,
       correlation_cholesky = correlation_cholesky,
       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)
}