mpower: Power Analysis via Monte Carlo Simulation for Correlated Data (original) (raw)
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 | |
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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 |
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