xgsu/glmMIC: Sparse Estimation of Generalized Linear Models (GLM) via Minimum approximated Inforamtion Criterion (MIC)

This packages computes sparse estimates for generalized linear models (GLM) via MIC, a short name for "Minimizing approxiamted Inforatmion Criterion". MIC mimics the best subset selection using a penalized likelihood approach yet with no need of a tuning parameter. The problem is further reformulated with a reparameterization step so that it reduces to an unconstrained nonconvex yet smooth programming problem, which can be solved efficiently. The global optimization algorithm, Simulated annealing (SA) or its generalized version (GenSA), is used for optimization, possibly combined with a local optimization algorithm (BFGS). Furthermore, the reparameterization tactic yields an additional advantage in terms of circumventing post-selection inference.

Getting started

Package details

AuthorXiaogang Su
MaintainerXiaogang Su <[email protected]l.com>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
xgsu/glmMIC documentation built on May 28, 2017, 7:36 a.m.