Ohit: OGA+HDIC+Trim and High-Dimensional Linear Regression Models

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.

Version: 1.0.0
Imports: stats
Published: 2017-09-06
Author: Hai-Tang Chiou, Ching-Kang Ing, Tze Leung Lai
Maintainer: Hai-Tang Chiou <htchiou1 at gmail.com>
License: GPL-2
URL: http://mx.nthu.edu.tw/~cking/pdf/IngLai2011.pdf
NeedsCompilation: no
CRAN checks: Ohit results

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Reference manual: Ohit.pdf
Package source: Ohit_1.0.0.tar.gz
Windows binaries: r-devel: Ohit_1.0.0.zip, r-release: Ohit_1.0.0.zip, r-oldrel: Ohit_1.0.0.zip
OS X El Capitan binaries: r-release: Ohit_1.0.0.tgz
OS X Mavericks binaries: r-oldrel: Ohit_1.0.0.tgz

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