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 
[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:
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