oem: Orthogonalizing EM

Solves penalized least squares problems for big tall data using the orthogonalizing EM algorithm of Xiong et al. (2016) <doi:10.1080/00401706.2015.1054436>. The main fitting function is oem() and the functions cv.oem() and xval.oem() are for cross validation, the latter being an accelerated cross validation function for linear models. The big.oem() function allows for out of memory fitting.

Version: 2.0.5
Depends: R (≥ 3.2.0), bigmemory
Imports: Rcpp (≥ 0.11.0), Matrix, foreach, methods
LinkingTo: Rcpp, RcppEigen, BH, bigmemory, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2017-03-22
Author: Bin Dai [aut], Jared Huling [aut, cre], Yixuan Qiu [ctb]
Maintainer: Jared Huling <jaredhuling at gmail.com>
BugReports: https://github.com/jaredhuling/oem/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/jaredhuling/oem
NeedsCompilation: yes
Materials: README
CRAN checks: oem results


Reference manual: oem.pdf
Vignettes: Usage of the oem Package
Package source: oem_2.0.5.tar.gz
Windows binaries: r-devel: oem_2.0.5.zip, r-release: oem_2.0.5.zip, r-oldrel: oem_2.0.5.zip
OS X El Capitan binaries: r-release: oem_2.0.5.tgz
OS X Mavericks binaries: r-oldrel: oem_2.0.5.tgz
Old sources: oem archive


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