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✨
DESCRIPTION
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

11.51 score 62 stars 22 packages 153 scripts 16k downloads 1 mentions 9 exports 9 dependencies

Last updated from:ffae358972. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK411
linux-devel-x86_64OK415
source / vignettesOK612
linux-release-arm64OK378
linux-release-x86_64OK394
macos-release-arm64OK208
macos-release-x86_64OK517
macos-oldrel-arm64OK317
macos-oldrel-x86_64OK847
windows-develOK487
windows-releaseOK443
windows-oldrelOK464
wasm-releaseFAIL202

Exports:fastglmfastglm_controlfastglm_fitfastglm_hurdlefastglm_nbfastglm_streamingfastglm_zifastglmPurenegbin

Dependencies:BHbigmemorybigmemory.sriFormulalatticeMatrixRcppRcppEigenuuid

Negative Binomial Stability Benchmark: fastglm vs MASS
Data-generating process | Scoring convergence | Running the benchmark | Results | References
Rendered fromnb-stability-fastglm.Rmdusingknitr::rmarkdown

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

Benchmark Study for 'fastglm'
Standard GLMs | Sparse and big.matrix paths | Negative-binomial regression | Firth bias-reduced GLMs | Firth across all decomposition methods | Firth on sparse and streaming designs | Hurdle models | Zero-inflated models | Summary | References
Rendered frombenchmarks-fastglm.Rmdusingknitr::rmarkdown

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

Negative Binomial Convergence: fastglm vs MASS
Data-generating process | Convergence comparison | Summary | Log-likelihood comparison | Theta estimates | Cases where only fastglm converges
Rendered fromnb-convergence-fastglm.Rmdusingknitr::rmarkdown

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

Overview of the 'fastglm' Package
Fitting a GLM | Decomposition methods | Stability | Inference: vcov(), robust SE, predictions | Sparse, big.matrix, and streaming designs | Native families | Negative binomial, hurdle, zero-inflation, and Firth logistic | Three R entry points | References
Rendered fromfastglm-overview.Rmdusingknitr::rmarkdown

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

Firth Bias-Reduced GLMs with 'fastglm'
Logistic regression under separation | General GLM families | Binomial (logit, probit, cloglog) | Poisson (log, sqrt) | Gamma (log, inverse) | Gaussian (identity, log) | Inverse Gaussian (log) | Standard errors | Penalized deviance | Speed comparison across families | References
Rendered fromfirth-fastglm.Rmdusingknitr::rmarkdown

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

Negative-Binomial, Hurdle, and Zero-Inflation with 'fastglm'
Negative-binomial regression | Hurdle models | Zero-inflated models | References
Rendered fromcount-firth-fastglm.Rmdusingknitr::rmarkdown

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

Quick Usage Guide to the 'fastglm' Package
Example | Computational stability
Rendered fromfastglm.Rmdusingknitr::rmarkdown

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

Large-Data and Out-of-Core GLMs with 'fastglm'
Sparse design matrices | Filebacked big.matrix | Streaming from an external source | arrow / parquet recipe | When to use which path
Rendered fromlarge-data-fastglm.Rmdusingknitr::rmarkdown

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