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-#' @title Fits the SparseStep model
-#'
-#' @description Fits the SparseStep model for a single value of the 
-#' regularization parameter.
-#'
-#' @param x matrix of predictors
-#' @param y response
-#' @param lambda regularization parameter
-#' @param gamma0 starting value of the gamma parameter
-#' @param gammastop stopping value of the gamma parameter
-#' @param IMsteps number of steps of the majorization algorithm to perform for 
-#' each value of gamma
-#' @param gammastep factor to decrease gamma with at each step
-#' @param normalize if TRUE, each variable is standardized to have unit L2 
-#' norm, otherwise it is left alone.
-#' @param intercept if TRUE, an intercept is included in the model (and not 
-#' penalized), otherwise no intercept is included
-#' @param force.zero if TRUE, absolute coefficients smaller than the provided 
-#' threshold value are set to absolute zero as a post-processing step, 
-#' otherwise no thresholding is performed
-#' @param threshold threshold value to use for setting coefficients to 
-#' absolute zero
-#' @param XX The X'X matrix; useful for repeated runs where X'X stays the same
-#' @param Xy The X'y matrix; useful for repeated runs where X'y stays the same
-#' @param use.XX whether or not to compute X'X and return it
-#' @param use.Xy whether or not to compute X'y and return it
-#'
-#' @return A "sparsestep" S3 object is returned, for which print, predict, 
-#' coef, and plot methods exist. It has the following items:
-#' \item{call}{The call that was used to construct the model.}
-#' \item{lambda}{The value(s) of lambda used to construct the model.}
-#' \item{gamma0}{The gamma0 value of the model.}
-#' \item{gammastop}{The gammastop value of the model}
-#' \item{IMsteps}{The IMsteps value of the model}
-#' \item{gammastep}{The gammastep value of the model}
-#' \item{intercept}{Boolean indicating if an intercept was fitted in the 
-#' model}
-#' \item{force.zero}{Boolean indicating if a force zero-setting was 
-#' performed.}
-#' \item{threshold}{The threshold used for a forced zero-setting}
-#' \item{beta}{The resulting coefficients stored in a sparse matrix format 
-#' (dgCMatrix). This matrix has dimensions nvar x nlambda}
-#' \item{a0}{The intercept vector for each value of gamma of length nlambda}
-#' \item{normx}{Vector used to normalize the columns of x}
-#' \item{meanx}{Vector of column means of x}
-#' \item{XX}{The matrix X'X if use.XX was set to TRUE}
-#' \item{Xy}{The matrix X'y if use.Xy was set to TRUE}
-#'
-#' @author
-#' Gertjan van den Burg (author and maintainer).
-#'
-#' @seealso
-#' \code{\link{coef}}, \code{\link{print}}, \code{\link{predict}}, 
-#' \code{\link{plot}}, and \code{\link{sparsestep.path}}.
-#'
-#' @export
-#'
-#' @examples
-#' x <- matrix(rnorm(100*20), 100, 20)
-#' y <- rnorm(100)
-#' fit <- sparsestep(x, y)
-#'
-sparsestep <- function(x, y, lambda=c(0.1, 0.5, 1.0, 5, 10), gamma0=1e3,
-		       gammastop=1e-4, IMsteps=2, gammastep=2.0,
-		       normalize=TRUE, intercept=TRUE, force.zero=TRUE,
-		       threshold=1e-7, XX=NULL, Xy=NULL, use.XX = TRUE,
-		       use.Xy = TRUE)
-{
-	call <- match.call()
-	prep <- preprocess(x, y, normalize, intercept, XX, Xy, use.XX, use.Xy)
-
-	betas <- NULL
-	for (lmd in lambda) {
-		beta <- run.sparsestep(prep$XX, prep$Xy, prep$nvars, lmd,
-				       gamma0, gammastep, gammastop, IMsteps,
-				       force.zero, threshold)
-		betas <- cbind(beta, betas)
-	}
-
-	post <- postprocess(t(betas), prep$a0, x, prep$normx, prep$nvars,
-			    length(lambda))
-
-	object <- list(call = call, lambda = lambda, gamma0 = gamma0,
-		       gammastop = gammastop, IMsteps = IMsteps,
-		       gammastep = gammastep, intercept = intercept,
-		       force.zero = force.zero, threshold = threshold,
-		       beta = post$beta, a0 = post$a0, normx = prep$normx,
-		       meanx = prep$meanx, XX = if(use.XX) prep$XX else NULL,
-		       Xy = if(use.Xy) prep$Xy else NULL)
-	class(object) <- "sparsestep"
-	return(object)
-}