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Quantitative characterization of the health impacts associated with exposure to chemical mixtures has received considerable attention in current environmental and epidemiological studies. 'CompMix' package allows practitioners to estimate the health impacts from exposure to chemical mixtures data through various statistical approaches, including Lasso, Elastic net, Bayeisan kernel machine regression (BKMR), hierNet, Quantile g-computation, Weighted quantile sum (WQS) and Random forest. Hao W, Cathey A, Aung M, Boss J, Meeker J, Mukherjee B. (2024) "Statistical methods for chemical mixtures: a practitioners guide". <doi:10.1101/2024.03.03.24303677>.
| Version: | 0.1.0 |
| Imports: | Matrix, mvtnorm, gglasso, higlasso, hierNet, glmnet, SuperLearner, bkmr, qgcomp, gWQS, pROC, randomForest, devtools |
| Published: | 2024-05-22 |
| DOI: | 10.32614/CRAN.package.CompMix |
| Author: | Wei Hao [aut, cre] |
| Maintainer: | Wei Hao <weihao at umich.edu> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | CompMix results |
| Reference manual: | CompMix.pdf |
| Package source: | CompMix_0.1.0.tar.gz |
| Windows binaries: | r-devel: CompMix_0.1.0.zip, r-release: CompMix_0.1.0.zip, r-oldrel: CompMix_0.1.0.zip |
| macOS binaries: | r-release (arm64): CompMix_0.1.0.tgz, r-oldrel (arm64): CompMix_0.1.0.tgz, r-release (x86_64): CompMix_0.1.0.tgz, r-oldrel (x86_64): CompMix_0.1.0.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.
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