speedglm: Fitting Linear and Generalized Linear Models to Large Data Sets

Fitting linear models and generalized linear models to large data sets by updating algorithms.

Version: 0.3-4
Depends: Matrix, MASS
Imports: methods, stats
Published: 2022-02-24
Author: Marco Enea [aut, cre], Ronen Meiri [ctb] (on behalf of DMWay Analytics LTD), Tomer Kalimi [ctb] (on behalf of DMWay Analytics LTD)
Maintainer: Marco Enea <marco.enea at unipa.it>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
Materials: NEWS
In views: HighPerformanceComputing
CRAN checks: speedglm results


Reference manual: speedglm.pdf


Package source: speedglm_0.3-4.tar.gz
Windows binaries: r-devel: speedglm_0.3-4.zip, r-release: speedglm_0.3-4.zip, r-oldrel: speedglm_0.3-4.zip
macOS binaries: r-release (arm64): speedglm_0.3-4.tgz, r-oldrel (arm64): speedglm_0.3-4.tgz, r-release (x86_64): speedglm_0.3-4.tgz, r-oldrel (x86_64): speedglm_0.3-4.tgz
Old sources: speedglm archive

Reverse dependencies:

Reverse depends: Rediscover
Reverse imports: alpine, bigstep, btergm, CytoGLMM, DMCFB, EventPointer, exomePeak2, GEint, ltmle, nullranges, PrInCE, tensorregress
Reverse suggests: broom, disk.frame, dynamichazard, insight, marginaleffects, mediation, parglm, scoringTools, SuperLearner, superMICE
Reverse enhances: fastlogitME, prediction, texreg


Please use the canonical form https://CRAN.R-project.org/package=speedglm to link to this page.