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

Documentation:

Reference manual:

speedglm.pdf

Downloads:

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:	GWASinlps, Rediscover
Reverse imports:	adapt4pv, allestimates, alpine, bigstep, btergm, chest, CytoGLMM, DMCFB, EventPointer, exomePeak2, GEint, hit, IFAA, LogisticDx, ltmle, nullranges, PrInCE, smurf, survtmle, tensorregress
Reverse suggests:	broom, disk.frame, dynamichazard, insight, marginaleffects, mediation, parglm, scoringTools, SuperLearner, superMICE
Reverse enhances:	fastlogitME, prediction, texreg

Linking:

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