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: 0.3-3
Depends: Matrix, MASS
Imports: methods, stats
Published: 2021-01-08
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 <emarco76 at libero.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_0.3-3.tar.gz
Windows binaries: r-devel: speedglm_0.3-3.zip, r-release: speedglm_0.3-3.zip, r-oldrel: speedglm_0.3-3.zip
macOS binaries: r-release (arm64): speedglm_0.3-3.tgz, r-release (x86_64): speedglm_0.3-3.tgz, r-oldrel: speedglm_0.3-3.tgz
Old sources: speedglm archive

Reverse dependencies:

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


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