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+#' SparseStep: Approximating the Counting Norm for Sparse Regularization
+#'
+#' In the SparseStep regression model the ordinary least-squares problem is 
+#' augmented with an approximation of the exact \eqn{\ell_0}{l[0]} pseudonorm.  
+#' This approximation is made increasingly more accurate in the SparseStep 
+#' algorithm, resulting in a sparse solution to the regression problem. See 
+#' the references for more information.
+#'
+#' @section SparseStep functions:
+#' The main SparseStep functions are:
+#' \describe{
+#' \item{\code{\link{sparsestep}}}{Fit a SparseStep model for a given range of 
+#' \eqn{\lambda} values}
+#' \item{\code{\link{path.sparsestep}}}{Fit the SparseStep model along a path 
+#' of \eqn{\lambda} values which are generated such that a model is created at 
+#' each possible level of sparsity, or until a given recursion depth is 
+#' reached.}
+#' }
+#'
+#' Other available functions are:
+#' \describe{
+#' \item{\code{\link{plot}}}{Plot the coefficient path of the SparseStep 
+#' model.}
+#' \item{\code{\link{predict}}}{Predict the outcome of the linear model using 
+#' SparseStep}
+#' \item{\code{\link{coef}}}{Get the coefficients from the SparseStep model}
+#' \item{\code{\link{print}}}{Print a short description of the SparseStep 
+#' model}
+#' }
+#'
+#' @author
+#' Gertjan van den Burg (author and maintainer).
+#'
+#' @name sparsestep-package
+#' @docType package
+#' @import Matrix
+NULL
+#>NULL