ncpen package fits the generalized linear models with
various nonconvex penalties. Supported regression models are Gaussian
(linear), binomial Logit (logistic), multinomial Logit, Poisson and Cox
proportional hazard. A 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). This package accepts a design matrix
X and vector of responses y, and
produces the regularization path over a grid of values for the tuning
parameter lambda. Also provides user-friendly processes for plotting,
selecting tuning parameters using cross-validation or generalized
information criterion (GIC), l2-regularization, penalty
weights, standardization and intercept. For a data set with many
variables (high-dimensional data), the algorithm selects relevant
variables producing a parsimonious regression model.
Related research paper can be found at ncpen paper. A recent manual is avaialbe at ncpen manual and for an example use, see ncepn example.
(This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine Graziadio Business School and the National Research Foundation of Korea.)
Authors
Dongshin Kim, Sunghoon Kwon, Sangin Lee
References * Kim, D., Lee, S. and Kwon, S. (2018). A unified algorithm for the non-convex penalized estimation: The ncpen package http://arxiv.org/abs/1811.05061. * Kwon, S., Lee, S. and Kim, Y. (2015) https://doi.org/10.1016/j.csda.2015.07.001, * Lee, S., Kwon, S. and Kim, Y. (2016) https://doi.org/10.1016/j.csda.2015.08.019.