Package: oem 2.0.12

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]

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oem.pdf |oem.html✨
oem/json (API)

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

Peer review:

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

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

On CRAN:

group-lassolassomachine-learningmcpoemoem-algorithmpenalized-regressionscadvariable-selection

6.02 score 27 stars 1 packages 26 scripts 368 downloads 1 mentions 7 exports 13 dependencies

Last updated 4 months agofrom:ada38fb888. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-win-x86_64OKOct 25 2024
R-4.5-linux-x86_64OKOct 25 2024
R-4.4-win-x86_64OKOct 25 2024
R-4.4-mac-x86_64OKOct 25 2024
R-4.4-mac-aarch64OKOct 25 2024
R-4.3-win-x86_64OKOct 25 2024
R-4.3-mac-x86_64OKOct 25 2024
R-4.3-mac-aarch64OKOct 25 2024

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

Dependencies:BHbigmemorybigmemory.sricodetoolsforeachiteratorslatticeMatrixRcppRcppArmadilloRcppEigenRSpectrauuid

Usage of the oem Package

Jared Huling

Rendered fromoem_vignette.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2022-10-06
Started: 2016-05-20