CompMix: A Comprehensive Toolkit for Environmental Mixtures Analysis ('CompMix')

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: NA
Author: Wei Hao [aut, cre]
Maintainer: Wei Hao <weihao at umich.edu>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: CompMix results

Documentation:

Reference manual: CompMix.pdf

Downloads:

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

Linking:

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