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diff --git a/man/print.sparsestep.Rd b/man/print.sparsestep.Rd
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+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/print.sparsestep.R
+\name{print.sparsestep}
+\alias{print.sparsestep}
+\title{Print the fitted SparseStep model}
+\usage{
+\method{print}{sparsestep}(obj, ...)
+}
+\arguments{
+\item{obj}{a "sparsestep" object to print}
+}
+\description{
+Prints a short text of a fitted SparseStep model
+}
+\examples{
+data(diabetes)
+attach(diabetes)
+object <- sparsestep(x, y)
+print(object)
+detach(diabetes)
+}
+
diff --git a/man/sparsestep.Rd b/man/sparsestep.Rd
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+++ b/man/sparsestep.Rd
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+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/fit.sparsestep.R, R/sparsestep.R
+\docType{package}
+\name{sparsestep}
+\alias{sparsestep}
+\alias{sparsestep-package}
+\title{Fits the SparseStep model}
+\usage{
+sparsestep(x, y, lambda = 1, gamma0 = 1e+06, gammastop = 1e-08,
+  IMsteps = 2, gammastep = 2, normalize = TRUE, intercept = TRUE,
+  force.zero = TRUE, threshold = 1e-07, XX = NULL, Xy = NULL,
+  use.XX = TRUE, use.Xy = TRUE)
+}
+\arguments{
+\item{x}{matrix of predictors}
+
+\item{y}{response}
+
+\item{lambda}{regularization parameter}
+
+\item{gamma0}{starting value of the gamma parameter}
+
+\item{gammastop}{stopping value of the gamma parameter}
+
+\item{IMsteps}{number of steps of the majorization algorithm to perform for
+each value of gamma}
+
+\item{gammastep}{factor to decrease gamma with at each step}
+
+\item{normalize}{if TRUE, each variable is standardized to have unit L2
+norm, otherwise it is left alone.}
+
+\item{intercept}{if TRUE, an intercept is included in the model (and not
+penalized), otherwise no intercept is included}
+
+\item{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}
+
+\item{threshold}{threshold value to use for setting coefficients to
+absolute zero}
+
+\item{XX}{The X'X matrix; useful for repeated runs where X'X stays the same}
+
+\item{Xy}{The X'y matrix; useful for repeated runs where X'y stays the same}
+
+\item{use.XX}{whether or not to compute X'X and return it}
+
+\item{use.Xy}{whether or not to compute X'y and return it}
+}
+\value{
+A "sparsestep" object is returned, for which predict, coef, methods
+exist.
+}
+\description{
+Fits the SparseStep model for a single value of the
+regularization parameter.
+
+sparsestep.
+}
+\examples{
+data(diabetes)
+attach(diabetes)
+object <- sparsestep(x, y)
+plot(object)
+detach(diabetes)
+}
+
diff --git a/man/sparsestep.cd.Rd b/man/sparsestep.cd.Rd
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+++ b/man/sparsestep.cd.Rd
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+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/cd.sparsestep.R
+\name{sparsestep.cd}
+\alias{sparsestep.cd}
+\title{Coordinate descent algorithm for SparseStep}
+\usage{
+sparsestep.cd(x, y, lambdas = NULL, epsilon = 1e-05, intercept = TRUE,
+  ...)
+}
+\description{
+Coordinate descent algorithm for SparseStep
+}
+
diff --git a/man/sparsestep.path.Rd b/man/sparsestep.path.Rd
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+++ b/man/sparsestep.path.Rd
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+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/path.sparsestep.R
+\name{sparsestep.path}
+\alias{sparsestep.path}
+\title{Path algorithm for the SparseStep model}
+\usage{
+sparsestep.path(x, y, max.depth = 10, intercept = TRUE, normalize = TRUE,
+  ...)
+}
+\arguments{
+\item{x}{matrix of predictors}
+
+\item{y}{response}
+
+\item{max.depth}{maximum recursion depth}
+
+\item{...}{further arguments to sparsestep()}
+}
+\description{
+Fits the entire regularization path for SparseStep using a
+Golden Section search.
+}
+