% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sparsestep-package.R
\docType{package}
\name{sparsestep-package}
\alias{sparsestep-package}
\title{SparseStep: Approximating the Counting Norm for Sparse Regularization}
\description{
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}
}
}

\examples{
x <- matrix(rnorm(100*20), 100, 20)
y <- rnorm(100)
fit <- sparsestep(x, y)
plot(fit)
fits <- path.sparsestep(x, y)
plot(fits)
x2 <- matrix(rnorm(50*20), 50, 20)
y2 <- predict(fits, x2)

}
\references{
Van den Burg, G.J.J., Groenen, P.J.F. and Alfons, A. (2017).
 \emph{SparseStep: Approximating the Counting Norm for Sparse Regularization},
 arXiv preprint arXiv:1701.06967 [stat.ME]. 
 URL \url{https://arxiv.org/abs/1701.06967}.
}
\author{
Gerrit J.J. van den Burg, Patrick J.F. Groenen, Andreas Alfons\cr
Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
}