#' @useDynLib smashr
#'
#' @title smashr: Smoothing using Adaptive SHrinkage in R
#'
#' @description This package performs nonparametric regression on
#' univariate Poisson or Gaussian data using multi-scale methods. For
#' the Poisson case, the data \eqn{x} is a vector, with \eqn{x_j \sim
#' Poi(\mu_j)} where the mean vector \eqn{\mu} is to be estimated.
#' For the Gaussian case, the data \eqn{x} are a vector with \eqn{x_j
#' \sim N(\mu_j, \sigma^2_j)}. Where the mean vector \eqn{\mu} and
#' variance vector \eqn{\sigma^2} are to be estimated. The primary
#' assumption is that \eqn{\mu} is spatially structured, so \eqn{\mu_j
#' - \mu_{j+1}} will often be small (that is, roughly, \eqn{\mu} is
#' smooth). Also \eqn{\sigma} is spatially structured in the Gaussian
#' case (or, optionally, \eqn{\sigma} is constant, not depending on
#' \eqn{j}).
#'
#' @details The function \code{\link{smash}} provides a minimal
#' interface to perform simple smoothing. It is actually a wrapper to
#' \code{\link{smash.gaus}} and \code{\link{smash.poiss}} which
#' provide more options for advanced use. The only required input is
#' a vector of length 2^J for some integer J. Other options include
#' the possibility of returning the posterior variances, specifying a
#' wavelet basis (default is Haar, which performs well in general due
#' to the fact that smash uses the translation-invariant transform)
#'
#' @author Matthew Stephens and Zhengrong Xing
#'
#' @docType package
#'
#' @name smashr
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