mpower: Power Analysis via Monte Carlo Simulation for Correlated Data

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 ORCID iD [aut, cre]
Maintainer: Phuc H. Nguyen <phuc.nguyen.rcran at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mpower results


Reference manual: mpower.pdf


Package source: mpower_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mpower_0.1.0.tgz, r-oldrel (arm64): mpower_0.1.0.tgz, r-release (x86_64): mpower_0.1.0.tgz, r-oldrel (x86_64): mpower_0.1.0.tgz


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