GlmSimulatoR: Creates Ideal Data for Generalized Linear Models

Have you ever struggled to find "good data" for a generalized linear model? Would you like to test how quickly statistics converge to parameters, or learn how picking different link functions affects model performance? This package creates ideal data for both common and novel generalized linear models so your questions can be empirically answered.

Version: 0.2
Imports: assertthat, stats, purrr, stringr, dplyr, statmod, magrittr, rlang, ggplot2, MASS, tweedie, cplm
Suggests: testthat, knitr, rmarkdown, covr
Published: 2019-11-28
Author: Greg McMahan
Maintainer: Greg McMahan <gmcmacran at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: GlmSimulatoR results

Downloads:

Reference manual: GlmSimulatoR.pdf
Vignettes: Count_Data_And_Overdispersion
Dealing_With_Right_Skewed_Data
Exploring_Links
Forward_Stepwise_Search
Introduction
Tweedie_Distribution
Package source: GlmSimulatoR_0.2.tar.gz
Windows binaries: r-devel: GlmSimulatoR_0.2.zip, r-devel-gcc8: GlmSimulatoR_0.2.zip, r-release: GlmSimulatoR_0.2.zip, r-oldrel: GlmSimulatoR_0.2.zip
OS X binaries: r-release: GlmSimulatoR_0.2.tgz, r-oldrel: GlmSimulatoR_0.2.tgz
Old sources: GlmSimulatoR archive

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