Package: parglm 0.2.0

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_0.2.0.tar.gz
parglm_0.2.0.zip(r-4.7)parglm_0.2.0.zip(r-4.6)parglm_0.2.0.zip(r-4.5)
parglm_0.2.0.tgz(r-4.6-x86_64)parglm_0.2.0.tgz(r-4.6-arm64)parglm_0.2.0.tgz(r-4.5-x86_64)parglm_0.2.0.tgz(r-4.5-arm64)
parglm_0.2.0.tar.gz(r-4.7-arm64)parglm_0.2.0.tar.gz(r-4.7-x86_64)parglm_0.2.0.tar.gz(r-4.6-arm64)parglm_0.2.0.tar.gz(r-4.6-x86_64)
parglm_0.2.0.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

6.66 score 2 packages 54 scripts 946 downloads 4 exports 5 dependencies

Last updated from:bb8cae372c. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK200
linux-devel-x86_64OK232
source / vignettesOK340
linux-release-arm64OK208
linux-release-x86_64OK236
macos-release-arm64OK111
macos-release-x86_64OK391
macos-oldrel-arm64OK99
macos-oldrel-x86_64OK233
windows-develOK184
windows-releaseOK219
windows-oldrelOK169
wasm-releaseOK207

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
Rendered fromparglm.Rmdusingknitr::rmarkdown

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
Rendered fromsandwich.Rmdusingknitr::rmarkdown

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