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: 1.2-1
Depends: R (≥ 2.10), GrassmannOptim, Matrix
Published: 2012-11-09
Author: Kofi Placid Adragni, Andrew Raim
Maintainer: Kofi Placid Adragni <kofi at umbc.edu>
License: GPL (≥ 2)
URL: http://www.math.umbc.edu/~kofi/ldr
NeedsCompilation: no
CRAN checks: ldr results

Downloads:

Package source: ldr_1.2-1.tar.gz
MacOS X binary: ldr_1.2-1.tgz
Windows binary: ldr_1.2-1.zip
Reference manual: ldr.pdf
Old sources: ldr archive