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+\name{rsc}
+
+\alias{rsc}
+
+\title{Robust and Sparse Correlation Matrix Estimator}
+
+\description{
+  Compute the Robust and Sparse Correlation Matrix (RSC) estimator 
+  proposed in Serra et al. (2018).
+}
+
+
+\usage{
+  rsc(cv, threshold = "minimum") 
+}
+
+
+\arguments{
+  \item{cv}{
+    An S3 object of class \code{"rsc_cv"} (see \code{\link{rsc_cv}}).
+  }
+  \item{threshold}{
+    Threshold parameter to compute the RSC estimate. This
+    is a numeric value taken onto the interval (0,1), or it is
+    equal to  \code{"minimum"} or \code{"minimum1se"} for selecting the
+    optimal threshold according to the selection performed in 
+    \code{\link{rsc_cv}}.
+   }
+}
+
+
+\details{
+   The setting \code{threshold = "minimum"} or \code{threshold =
+   "minimum1se"} applies thresholding according to the criteria
+   discussed in the \emph{Details} section in \code{\link{rsc_cv}}.
+   When \code{cv} is obtained using \code{\link{rsc_cv}} with
+   \code{cv.type = "random"}, the default settings for \code{\link{rsc}}
+   implements exactly the RSC estimator proposed in Serra et al.,
+   (2018).
+  
+   Although \code{threshold = "minimum"} is the default choice, in
+   high-dimensional situations \code{threshold = "minimum1se"} usually
+   provides a more parsimonious representation of the correlation
+   structure. Since the underlying RMAD matrix is passed through the
+   \code{cv} input, any other hand-tuned threshold to the RMAD matrix
+   can be applied without significant additional computational
+   costs. The latter can be done setting \code{threshold} to any value
+   onto the (0,1) interval.
+
+   The software is optimized to handle high-dimensional data sets,
+   therefore, the output RSC matrix is packed into a storage efficient
+   sparse format using the \code{"dsCMatrix"} S4 class from the
+   \code{\link{Matrix}} package. The latter is specifically designed for
+   sparse real symmetric matrices.
+}
+
+
+
+\value{
+  Returns a sparse correlaiton matrix of class \code{"dsCMatrix"}
+  (S4 class object) as defined in the \code{\link{Matrix}} package. 
+}
+
+
+
+
+\section{References}{
+   Serra, A., Coretto, P., Fratello, M., and Tagliaferri, R. (2018).
+   Robust and sparsecorrelation matrix estimation for the analysis of
+   high-dimensional genomics data. \emph{Bioinformatics}, 34(4),
+   625-634.  doi:10.1093/bioinformatics/btx642
+}
+
+
+
+\seealso{
+  \code{\link{rsc_cv}}
+}
+
+
+
+
+
+
+
+\examples{
+\donttest{
+## simulate a random sample from a multivariate Cauchy distribution
+## note: example in high-dimension are obtained increasing p
+set.seed(1)
+n   <- 100  # sample size
+p   <- 10   # dimension
+dat <- matrix(rt(n*p, df = 1), nrow = n, ncol = p)
+colnames(dat) <- paste0("Var", 1:p)
+
+   
+## perform 10-fold cross-validation repeated R=10 times
+## note: for multi-core machines experiment with 'ncores'
+set.seed(2)
+a <- rsc_cv(x = dat, R = 10, K = 10)
+a
+
+## obtain the RSC matrix with "minimum" flagged solution 
+b <- rsc(cv = a, threshold = "minimum")
+b
+   
+## obtain the RSC matrix with "minimum1se" flagged solution 
+d <- rsc(cv = a, threshold = "minimum1se")
+d
+
+## since the object 'a' stores the RMAD underlying estimator, we can
+## apply thresholding at any level without re-estimating the RMAD 
+## matrix
+e <- rsc(cv = a, threshold = 0.5)
+e
+}
+}
+