Package: oem
Type: Package
Title: Orthogonalizing EM: Penalized Regression for Big Tall Data
Version: 2.1.0
Authors@R: c(
    person("Bin", "Dai", , "bdai@uwalumni.com", role = c("aut")),
    person("Jared", "Huling", , "jaredhuling@gmail.com", c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0670-4845")),
    person("Yixuan", "Qiu", , , c("ctb")),
    person("Gael", "Guennebaud", , , c("cph")),
    person("Jitse", "Niesen", , , c("cph"))
    )
Maintainer: Jared Huling <jaredhuling@gmail.com>
Description: 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>.
URL: https://arxiv.org/abs/1801.09661,
        https://github.com/jaredhuling/oem,
        https://jaredhuling.org/oem/
BugReports: https://github.com/jaredhuling/oem/issues
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 3.2.0), bigmemory
Imports: Rcpp (>= 0.11.0), Matrix, foreach, methods
LinkingTo: Rcpp, RcppEigen, BH, RSpectra (>= 0.16-2), bigmemory,
        RcppArmadillo
RoxygenNote: 7.3.3
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), glmnet
VignetteBuilder: knitr
Config/testthat/edition: 3
Repository: https://jaredhuling.r-universe.dev
Date/Publication: 2026-11-10 18:57:57 UTC
RemoteUrl: https://github.com/jaredhuling/oem
RemoteRef: HEAD
RemoteSha: 3a4c596188e09dd2196f11491cacbdd9517ee089
NeedsCompilation: yes
Packaged: 2026-11-10 19:43:50 UTC; root
Author: Bin Dai [aut],
  Jared Huling [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-0670-4845>),
  Yixuan Qiu [ctb],
  Gael Guennebaud [cph],
  Jitse Niesen [cph]