R/gcdnet-package.R

In gcdnet: The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm

##' FHT data introduced in Friedman et al. (2010).
##'
##' The \code{FHT} data set has n = 50 observations and p = 100 predictors. The
##' covariance between predictors Xj and Xj' has the same correlation 0.5. See
##' details in Friedman et al. (2010).
##'
##' @name FHT
##' @docType data
##' @format This data frame contains the following columns: \describe{
##'   \item{x}{a matrix with 100 rows and 5000 columns} \item{y}{class labels}
##'   \item{y_reg}{response variable for regression} }
##' @references Yang, Y. and Zou, H. (2012).
##'   "An Efficient Algorithm for Computing The HHSVM and Its Generalizations."
##'   \emph{Journal of Computational and Graphical Statistics}, 22, 396-415.\cr
##'   BugReport: \url{https://github.com/emeryyi/gcdnet}\cr
##'
##'   Gu, Y., and Zou, H. (2016).
##'   "High-dimensional generalizations of asymmetric least squares regression and their applications."
##'   \emph{The Annals of Statistics}, 44(6), 2661–2694.\cr
##'
##'   Friedman, J., Hastie, T., and Tibshirani, R. (2010).
##'   "Regularization paths for generalized linear models via coordinate descent."
##'   \emph{Journal of Statistical Software, 33, 1.}\cr
##'   \url{https://www.jstatsoft.org/v33/i01/}
##' @keywords datasets
##' @examples
##'
##' data(FHT)
##'
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