Package: ncpen
Type: Package
Title: Nonconvex Penalized Estimation for Generalized Linear Models
Version: 0.2.0
Date: 2018-02-21
Authors@R: c(person("Dongshin", "Kim", email = "dongshin.kim@live.com",role = c("aut", "cre", "cph")),
             person("Sunghoon", "Kwon", email = "shkwon0522@gmail.com",role = c("aut", "cph")),
             person("Sangin", "Lee", email = "sanginlee44@gmail.com",role = c("aut", "cph")))
Description: An efficient unified algorithm for estimating the nonconvex penalized
    linear, logistic and Poisson regression models. The unified algorithm is implemented
    based on the convex concave procedure and
    the algorithm can be applied to most of the existing nonconvex penalties.
    The algorithm also supports convex penalty:
    least absolute shrinkage and selection operator (LASSO).
    Supported nonconvex penalties include
    smoothly clipped absolute deviation (SCAD),
    minimax concave penalty (MCP), truncated LASSO penalty (TLP),
    clipped LASSO (CLASSO), sparse ridge (SRIDGE),
    modified bridge (MBRIDGE) and modified log (MLOG).
    For a data set with many variables (high-dimensional data),
    the algorithm selects relevant variables producing a parsimonious regression model.
    Kwon, S., Lee, S. and Kim, Y. (2015) <doi:10.1016/j.csda.2015.07.001>,
    Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>.
    (This project is funded by Julian Virtue Professorship from Center for Applied Research at
    Graziadio School of Business and Management at Pepperdine University.)
License: GPL (>= 3)
URL: https://github.com/zeemkr/ncpen
BugReports: https://github.com/zeemkr/ncpen/issues
LazyData: TRUE
Imports: Rcpp (>= 0.11.2)
LinkingTo: Rcpp, RcppArmadillo
Depends: R(>= 3.4)
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-02-21 16:55:34 UTC; dongshin
Author: Dongshin Kim [aut, cre, cph],
  Sunghoon Kwon [aut, cph],
  Sangin Lee [aut, cph]
Maintainer: Dongshin Kim <dongshin.kim@live.com>
Repository: CRAN
Date/Publication: 2018-02-21 18:36:08 UTC
