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. A description of the underlying methods and code interface is described in Huling and Chien (2022) <doi:10.18637/jss.v104.i06>.">

oem: Orthogonalizing EM: Penalized Regression for Big Tall Data (original) (raw)

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. A description of the underlying methods and code interface is described in Huling and Chien (2022) <doi:10.18637/jss.v104.i06>.

Version: 2.0.12
Depends: R (≥ 3.2.0), bigmemory
Imports: Rcpp (≥ 0.11.0), Matrix, foreach, methods
LinkingTo: Rcpp, RcppEigen, BH, RSpectra (≥ 0.16-2), bigmemory, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2024-07-31
DOI: 10.32614/CRAN.package.oem
Author: Bin Dai [aut], Jared Huling ORCID iD [aut, cre], Yixuan Qiu [ctb], Gael Guennebaud [cph], Jitse Niesen [cph]
Maintainer: Jared Huling
BugReports: https://github.com/jaredhuling/oem/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://arxiv.org/abs/1801.09661,https://github.com/jaredhuling/oem,https://jaredhuling.org/oem/
NeedsCompilation: yes
Citation: oem citation info
Materials: README
CRAN checks: oem results

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