ldr: Methods for likelihood-based dimension reduction in regression

Functions, methods, and datasets for fitting likelihood-based dimension reduction in regression, using principal fitted components (pfc), likelihood acquired directions (lad), covariance reducing models (core).

Version: 1.3
Depends: R (≥ 2.10), GrassmannOptim, Matrix
Published: 2014-01-03
Author: Kofi Placid Adragni, Andrew Raim
Maintainer: Kofi Placid Adragni <kofi at umbc.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.math.umbc.edu/~kofi/ldr
NeedsCompilation: no
CRAN checks: ldr results

Downloads:

Reference manual: ldr.pdf
Package source: ldr_1.3.tar.gz
OS X binary: ldr_1.3.tgz
Windows binary: ldr_1.3.zip
Old sources: ldr archive