threeboost: Thresholded variable selection and prediction based on estimating equations

This package implements a thresholded version of the EEBoost algorithm described in [Wolfson (2011, JASA)]. EEBoost is a general-purpose method for variable selection which can be applied whenever inference would be based on an estimating equation. The package currently implements variable selection based on the Generalized Estimating Equations, but can also accommodate user-provided estimating functions. Thresholded EEBoost is a generalization which allows multiple variables to enter the model at each boosting step.

Version: 1.1
Imports: Matrix
Suggests: mvtnorm
Published: 2014-08-11
Author: Julian Wolfson and Christopher Miller
Maintainer: Julian Wolfson <julianw at umn.edu>
License: GPL-3
NeedsCompilation: no
CRAN checks: threeboost results

Downloads:

Reference manual: threeboost.pdf
Package source: threeboost_1.1.tar.gz
Windows binaries: r-devel: threeboost_1.1.zip, r-release: threeboost_1.1.zip, r-oldrel: threeboost_1.1.zip
OS X Snow Leopard binaries: r-release: threeboost_1.1.tgz, r-oldrel: threeboost_1.1.tgz
OS X Mavericks binaries: r-release: threeboost_1.1.tgz
Old sources: threeboost archive