A flexible framework for power analysis using Monte Carlo simulation for settings in which considerations of the correlations between predictors are important. Users can set up a data generative model that preserves dependence structures among predictors given existing data (continuous, binary, or ordinal). Users can also generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This package includes several statistical models common in environmental mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <doi:10.48550/arXiv.2209.08036>.
Version: 0.1.0 Depends: R (≥ 3.5.0) Imports: abind, boot, dplyr, doSNOW, foreach, ggplot2, MASS, magrittr, parallel, purrr, snow, sbgcop, rlang, reshape2, tibble, tidyr, tidyselect Suggests: BMA, bkmr, bws, infinitefactor, knitr, NHANES, qgcomp, rmarkdown, rstan, testthat, openxlsx Published: 2022-09-21 DOI: 10.32614/CRAN.package.mpower Author: Phuc H. Nguyen [aut, cre] Maintainer: Phuc H. Nguyen <phuc.nguyen.rcran at gmail.com> License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] NeedsCompilation: no Materials: README NEWS CRAN checks: mpower results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=mpower to link to this page.
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