Package: oem 2.0.12

Jared Huling

oem: Orthogonalizing EM: Penalized Regression for Big Tall Data

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>.

Authors:Bin Dai [aut], Jared Huling [aut, cre], Yixuan Qiu [ctb], Gael Guennebaud [cph], Jitse Niesen [cph]

oem_2.0.12.tar.gz
oem_2.0.12.tar.gz(r-4.5-noble)oem_2.0.12.tar.gz(r-4.4-noble)
oem_2.0.12.tgz(r-4.4-emscripten)oem_2.0.12.tgz(r-4.3-emscripten)
oem.pdf |oem.html✨
oem/json (API)

# Install 'oem' in R:
install.packages('oem', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jaredhuling/oem/issues

Pkgdown site:https://jaredhuling.org

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

openblascppopenmp

2.70 score 353 downloads 1 mentions 7 exports 13 dependencies

Last updated 6 months agofrom:98789746bc. Checks:2 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKJan 28 2025
R-4.5-linux-x86_64OKJan 28 2025

Exports:big.oemcv.oemcv.oemfitoemoem.xtxoemfitxval.oem

Dependencies:BHbigmemorybigmemory.sricodetoolsforeachiteratorslatticeMatrixRcppRcppArmadilloRcppEigenRSpectrauuid

Usage of the oem Package

Jared Huling

Rendered fromoem_vignette.Rmdusingknitr::rmarkdownon Jan 28 2025.

Last update: 2022-10-13
Started: 2016-10-19