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: 0.1.0
Imports: assertthat, stats, purrr, stringr, dplyr, statmod, magrittr, rlang, ggplot2, MASS
Suggests: testthat, knitr, rmarkdown
Published: 2019-08-12
Author: Greg McMahan
Maintainer: Greg McMahan <gmcmacran at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: GlmSimulatoR results

Downloads:

Reference manual: GlmSimulatoR.pdf
Vignettes: Dealing With Right Skewed Data
Exploring_Links
Forward Stepwise Search
Introduction
Package source: GlmSimulatoR_0.1.0.tar.gz
Windows binaries: r-devel: GlmSimulatoR_0.1.0.zip, r-release: GlmSimulatoR_0.1.0.zip, r-oldrel: GlmSimulatoR_0.1.0.zip
OS X binaries: r-release: GlmSimulatoR_0.1.0.tgz, r-oldrel: GlmSimulatoR_0.1.0.tgz

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