Package: oem
Type: Package
Title: Orthogonalizing EM: Penalized Regression for Big Tall Data
Version: 2.0.11
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, bigmemory, RcppArmadillo
RoxygenNote: 7.2.0
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2022-10-12 14:11:31 UTC; huling
Author: Bin Dai [aut],
  Jared Huling [aut, cre] (<https://orcid.org/0000-0003-0670-4845>),
  Yixuan Qiu [ctb],
  Gael Guennebaud [cph],
  Jitse Niesen [cph]
Repository: CRAN
Date/Publication: 2022-10-13 08:20:08 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 01:28:22 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
