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Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.
| Version: | 0.33.1 |
| Depends: | R (≥ 3.4.0) |
| Imports: | numDeriv |
| Suggests: | knitr, rmarkdown |
| Published: | 2020-06-12 |
| DOI: | 10.32614/CRAN.package.gim |
| Author: | Han Zhang, Kai Yu |
| Maintainer: | Han Zhang <zhangh.ustc at gmail.com> |
| BugReports: | https://github.com/zhangh12/gim/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/zhangh12/gim |
| NeedsCompilation: | no |
| CRAN checks: | gim results |
| Reference manual: | gim.pdf |
| Vignettes: |
Generalized Integration Model |
| Package source: | gim_0.33.1.tar.gz |
| Windows binaries: | r-devel: gim_0.33.1.zip, r-release: gim_0.33.1.zip, r-oldrel: gim_0.33.1.zip |
| macOS binaries: | r-release (arm64): gim_0.33.1.tgz, r-oldrel (arm64): gim_0.33.1.tgz, r-release (x86_64): gim_0.33.1.tgz, r-oldrel (x86_64): gim_0.33.1.tgz |
| Old sources: | gim archive |
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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.