add docs to git

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diff --git a/man/coef.sparsestep.Rd b/man/coef.sparsestep.Rd
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+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/coef.sparsestep.R
+\name{coef.sparsestep}
+\alias{coef}
+\alias{coef.sparsestep}
+\title{Get the coefficients of a fitted SparseStep model}
+\usage{
+\method{coef}{sparsestep}(object, ...)
+}
+\arguments{
+\item{object}{a \code{sparsestep} object}
+
+\item{\dots}{further argument are ignored}
+}
+\value{
+The coefficients of the SparseStep model (i.e. the betas). If the
+model was fitted with an intercept this will be the first value in the
+resulting vector.
+}
+\description{
+Returns the coefficients of the SparseStep model.
+}
+\examples{
+x <- matrix(rnorm(100*20), 100, 20)
+y <- rnorm(100)
+fit <- sparsestep(x, y)
+coef(fit)
+}
+
diff --git a/man/path.sparsestep.Rd b/man/path.sparsestep.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{path.sparsestep}
+\alias{path.sparsestep}
+\title{Approximate path algorithm for the SparseStep model}
+\usage{
+path.sparsestep(x, y, max.depth = 10, gamma0 = 1000, gammastop = 1e-04,
+  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{max.depth}{maximum recursion depth}
+
+\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" 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}
+}
+\description{
+Fits the entire regularization path for SparseStep using a
+Golden Section search. Note that this algorithm is approximate, there is no
+guarantee that the solutions _between_ induced values of lambdas do not
+differ from those calculated. For instance, if solutions are calculated at
+\eqn{\lambda_{i}}{\lambda[i]} and \eqn{\lambda_{i+1}}{\lambda[i+1]}, this
+algorithm ensures that \eqn{\lambda_{i+1}}{\lambda[i+1]} has one more zero
+than the solution at \eqn{\lambda_{i}}{\lambda[i]} (provided the recursion
+depth is large enough). There is however no guarantee that there are no
+different solutions between \eqn{\lambda_{i}}{\lambda[i]} and
+\eqn{\lambda_{i+1}}{\lambda[i+1]}.  This is an ongoing research topic.
+
+Note that this path algorithm is not faster than running the
+\code{sparsestep} function with the same \eqn{\lambda} sequence.
+}
+\examples{
+x <- matrix(rnorm(100*20), 100, 20)
+y <- rnorm(100)
+pth <- path.sparsestep(x, y)
+}
+\seealso{
+\code{\link{coef}}, \code{\link{print}}, \code{\link{predict}},
+\code{\link{plot}}, and \code{\link{sparsestep}}.
+}
+
diff --git a/man/plot.sparsestep.Rd b/man/plot.sparsestep.Rd
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+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/plot.sparsestep.R
+\name{plot.sparsestep}
+\alias{plot}
+\alias{plot.sparsestep}
+\title{Plot the SparseStep path}
+\usage{
+\method{plot}{sparsestep}(x, ...)
+}
+\arguments{
+\item{x}{a \code{sparsestep} object}
+
+\item{\dots}{further argument to matplot}
+}
+\description{
+Plot the coefficients of the SparseStep path
+}
+\examples{
+x <- matrix(rnorm(100*20), 100, 20)
+y <- rnorm(100)
+fit <- sparsestep(x, y)
+plot(fit)
+pth <- path.sparsestep(x, y)
+plot(pth)
+}
+
diff --git a/man/predict.sparsestep.Rd b/man/predict.sparsestep.Rd
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+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/predict.sparsestep.R
+\name{predict.sparsestep}
+\alias{predict}
+\alias{predict.sparsestep}
+\title{Make predictions from a SparseStep model}
+\usage{
+\method{predict}{sparsestep}(object, newx, ...)
+}
+\arguments{
+\item{object}{Fitted \code{sparsestep} object}
+
+\item{newx}{Matrix of new values for \code{x} at which predictions are to
+be made.}
+
+\item{\dots}{further argument are ignored}
+}
+\value{
+a matrix of numerical predictions of size nobs x nlambda
+}
+\description{
+Predicts the outcome variable for the SparseStep model for
+each value of lambda supplied to the model.
+}
+\examples{
+x <- matrix(rnorm(100*20), 100, 20)
+y <- rnorm(100)
+fit <- sparsestep(x, y)
+yhat <- predict(fit, x)
+}
+
diff --git a/man/sparsestep-package.Rd b/man/sparsestep-package.Rd
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+% Generated by roxygen2 (4.1.1): 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}
+}
+}
+\author{
+Gertjan van den Burg (author and maintainer).
+}
+