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Implements a regularized Bayesian estimator that optimizes the estimation of between-group coefficients for multilevel latent variable models by minimizing mean squared error (MSE) and balancing variance and bias. The package provides more reliable estimates in scenarios with limited data, offering a robust solution for accurate parameter estimation in two-level latent variable models. It is designed for researchers in psychology, education, and related fields who face challenges in estimating between-group effects under small sample sizes and low intraclass correlation coefficients. Dashuk et al. (2024) <doi:10.13140/RG.2.2.18148.39048> derived the optimal regularized Bayesian estimator; Dashuk et al. (2024) <doi:10.13140/RG.2.2.34350.01604> extended it to the multivariate case; and Luedtke et al. (2008) <doi:10.1037/a0012869> formalized the two-level latent variable framework.
| Version: | 0.0.1.6 |
| Depends: | R (≥ 4.1.0) |
| Imports: | pracma |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2025-05-29 |
| DOI: | 10.32614/CRAN.package.MultiLevelOptimalBayes |
| Author: | Valerii Dashuk [aut, cre], Binayak Timilsina [aut], Martin Hecht [aut], Steffen Zitzmann [aut] |
| Maintainer: | Valerii Dashuk <vadashuk at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | MultiLevelOptimalBayes results |
| Reference manual: | MultiLevelOptimalBayes.pdf |
| Vignettes: |
MultiLevelOptimalBayes-Intro (source, R code) |
| Package source: | MultiLevelOptimalBayes_0.0.1.6.tar.gz |
| Windows binaries: | r-devel: not available, r-release: MultiLevelOptimalBayes_0.0.1.5.zip, r-oldrel: MultiLevelOptimalBayes_0.0.1.5.zip |
| macOS binaries: | r-release (arm64): MultiLevelOptimalBayes_0.0.1.6.tgz, r-oldrel (arm64): MultiLevelOptimalBayes_0.0.1.6.tgz, r-release (x86_64): MultiLevelOptimalBayes_0.0.1.6.tgz, r-oldrel (x86_64): MultiLevelOptimalBayes_0.0.1.5.tgz |
| Old sources: | MultiLevelOptimalBayes archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.