Package: parglm 0.1.10

parglm: Parallel GLM

Provides a parallel estimation method for generalized linear models without compiling with a multithreaded LAPACK or BLAS.

Authors:Benjamin Christoffersen [aut], Anthony Williams [cph], Boost developers [cph], Tom Palmer [aut, cre]

parglm_0.1.10.tar.gz
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parglm_0.1.10.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
parglm/json (API)

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

Bug tracker:https://github.com/remlapmot/parglm/issues

Pkgdown/docs site:https://remlapmot.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

6.60 score 2 packages 54 scripts 1.0k downloads 4 exports 5 dependencies

Last updated from:4f6ad5ca04. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK393
linux-devel-x86_64OK412
source / vignettesOK280
linux-release-arm64OK209
linux-release-x86_64OK176
macos-release-arm64OK129
macos-release-x86_64OK481
macos-oldrel-arm64OK111
macos-oldrel-x86_64OK367
windows-develOK235
windows-releaseOK182
windows-oldrelOK176
wasm-releaseOK157

Exports:parglmparglm.controlparglm.fittidy_parglm_robust

Dependencies:latticeMatrixparallellyRcppRcppArmadillo

Introduction to the parglm package
Example of computation time | Smaller datasets | n = 100,000 | n = 10,000 | Varying the number of coefficients | n = 100,000, p = 5 | n = 1,000,000, p = 20 | Session info

Last update: 2026-05-14
Started: 2018-11-16

Robust standard errors with parglm and the sandwich package and regression tables with gtsummary
Setup | Fitting the model | Standard errors | Heteroskedasticity-consistent (HC) standard errors | Cluster-robust standard errors | Covariance matrices directly | Note | Regression tables with gtsummary | Logistic regression | Robust standard errors in a gtsummary table

Last update: 2026-05-12
Started: 2026-05-10