Package: fastglm 0.1.1

fastglm: Fast and Stable Fitting of Generalized Linear Models using 'RcppEigen'

Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard against convergence issues.

Authors:Jared Huling [aut, cre], Douglas Bates [cph], Dirk Eddelbuettel [cph], Romain Francois [cph], Yixuan Qiu [cph], Noah Greifer [ctb]

fastglm_0.1.1.tar.gz
fastglm_0.1.1.zip(r-4.7)fastglm_0.1.1.zip(r-4.6)fastglm_0.1.1.zip(r-4.5)
fastglm_0.1.1.tgz(r-4.6-x86_64)fastglm_0.1.1.tgz(r-4.6-arm64)fastglm_0.1.1.tgz(r-4.5-x86_64)fastglm_0.1.1.tgz(r-4.5-arm64)
fastglm_0.1.1.tar.gz(r-4.7-arm64)fastglm_0.1.1.tar.gz(r-4.7-x86_64)fastglm_0.1.1.tar.gz(r-4.6-arm64)fastglm_0.1.1.tar.gz(r-4.6-x86_64)
fastglm_0.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
fastglm/json (API)

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

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

Pkgdown/docs site:https://jaredhuling.org

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

On CRAN:

Conda:

cpp

11.44 score 62 stars 22 packages 133 scripts 16k downloads 1 mentions 9 exports 9 dependencies

Last updated from:a95cb08033. Checks:11 WARNING, 1 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING392
linux-devel-x86_64WARNING386
source / vignettesOK639
linux-release-arm64WARNING390
linux-release-x86_64WARNING364
macos-release-arm64WARNING251
macos-release-x86_64WARNING591
macos-oldrel-arm64WARNING318
macos-oldrel-x86_64WARNING776
windows-develWARNING505
windows-releaseWARNING478
windows-oldrelWARNING512
wasm-releaseFAIL222

Exports:fastglmfastglm_controlfastglm_fitfastglm_hurdlefastglm_nbfastglm_streamingfastglm_zifastglmPurenegbin

Dependencies:BHbigmemorybigmemory.sriFormulalatticeMatrixRcppRcppEigenuuid

Benchmark Study for 'fastglm'

Jared Huling

Rendered frombenchmarks-fastglm.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-05-27
Started: 2026-05-01

Firth Bias-Reduced GLMs with 'fastglm'

Jared Huling

Rendered fromfirth-fastglm.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-05-13
Started: 2026-05-13

Large-Data and Out-of-Core GLMs with 'fastglm'

Jared Huling

Rendered fromlarge-data-fastglm.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-05-01
Started: 2026-05-01

Negative Binomial Convergence: fastglm vs MASS

Jared Huling

Rendered fromnb-convergence-fastglm.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-05-27
Started: 2026-05-15

Negative Binomial Stability Benchmark: fastglm vs MASS

Jared Huling

Rendered fromnb-stability-fastglm.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-06-02
Started: 2026-06-02

Negative-Binomial, Hurdle, and Zero-Inflation with 'fastglm'

Jared Huling

Rendered fromcount-firth-fastglm.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-05-13
Started: 2026-05-01

Overview of the 'fastglm' Package

Jared Huling

Rendered fromfastglm-overview.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-05-27
Started: 2026-05-01

Quick Usage Guide to the 'fastglm' Package

Jared Huling

Rendered fromfastglm.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-05-03
Started: 2025-12-16