MultiLevelOptimalBayes: Regularized Bayesian Estimator for Two-Level Latent Variable
Models
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. The package includes comprehensive S3 methods for result
objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(),
dim(), length(), names(), and update() for enhanced usability and integration
with standard R workflows. 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: |
0.0.3.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
pracma |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2025-09-11 |
| 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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=MultiLevelOptimalBayes
to link to this page.