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#' @name Hyde-package
#' @aliases Hyde
#'
#' @title Hydbrid Decision Networks
#' @description Facilities for easy implementation of hybrid Bayesian networks
#' using R. Bayesian networks are directed acyclic graphs representing joint
#' probability distributions, where each node represents a random variable and
#' each edge represents conditionality. The full joint distribution is
#' therefore factorized as a product of conditional densities, where each node
#' is assumed to be independent of its non-descendants given information on its
#' parent nodes. Since exact, closed-form algorithms are computationally
#' burdensome for inference within hybrid networks that contain a combination
#' of continuous and discrete nodes, particle-based approximation techniques
#' like Markov Chain Monte Carlo are popular. We provide a user-friendly
#' interface to constructing these networks and running inference using rjags.
#' Econometric analyses (maximum expected utility under competing policies,
#' value of information) involving decision and utility nodes are also
#' supported.
#'
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