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 R package gcdnet version 1.0.6 Implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression. Software Imports: grDevices, graphics, stats, methods, Matrix Suggests: testthat Yi Yang <yi.yang6@mcgill.ca> Comprehensive R Archive Network (CRAN) Yi Yang <yi.yang6@mcgill.ca>, Yuwen Gu <yuwen.gu@uconn.edu>, Hui Zou <hzou@stat.umn.edu> GPL (>= 2) 2022-08-14 application/tgz https://CRAN.R-project.org/package=gcdnet doi:10.32614/CRAN.package.gcdnet