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Contains methods for fitting Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs). Generalized regression models are common methods for handling data for which assuming Gaussian-distributed errors is not appropriate. For instance, if the response of interest is binary, count, or proportion data, one can instead model the expectation of the response based on an appropriate data-generating distribution. This package provides methods for fitting GLMs and GAMs under Beta regression, Poisson regression, Gamma regression, and Binomial regression (currently GLM only) settings. Models are fit using local scoring algorithms described in Hastie and Tibshirani (1990) <doi:10.1214/ss/1177013604>.
| Version: | 1.0.2 |
| Imports: | gam, stats |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2024-07-09 |
| DOI: | 10.32614/CRAN.package.glam |
| Author: | Andrew Cooper [aut, cre, cph] |
| Maintainer: | Andrew Cooper <ahcooper at vt.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | glam results |
| Reference manual: | glam.pdf |
| Vignettes: |
glam |
| Package source: | glam_1.0.2.tar.gz |
| Windows binaries: | r-devel: glam_1.0.2.zip, r-release: glam_1.0.2.zip, r-oldrel: glam_1.0.2.zip |
| macOS binaries: | r-release (arm64): glam_1.0.2.tgz, r-oldrel (arm64): glam_1.0.2.tgz, r-release (x86_64): glam_1.0.2.tgz, r-oldrel (x86_64): glam_1.0.2.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.