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 |
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