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: | 0.2.3 |
| Imports: | assertthat, stats, purrr, stringr, dplyr, statmod, magrittr, MASS, tweedie, ggplot2, cplm |
| Suggests: | testthat, knitr, rmarkdown, covr |
| Published: | 2020-06-16 |
| Author: | Greg McMahan |
| Maintainer: | Greg McMahan <gmcmacran at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | GlmSimulatoR results |
| Reference manual: | GlmSimulatoR.pdf |
| Vignettes: |
Count_Data_And_Overdispersion Dealing_With_Right_Skewed_Data Exploring_Links Introduction Stepwise_Search Tweedie_Distribution |
| Package source: | GlmSimulatoR_0.2.3.tar.gz |
| Windows binaries: | r-devel: GlmSimulatoR_0.2.3.zip, r-release: GlmSimulatoR_0.2.3.zip, r-oldrel: GlmSimulatoR_0.2.3.zip |
| macOS binaries: | r-release: GlmSimulatoR_0.2.3.tgz, r-oldrel: GlmSimulatoR_0.2.3.tgz |
| Old sources: | GlmSimulatoR archive |
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