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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=CompMix to link to this page.
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