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) derived the optimal regularized Bayesian estimator; Dashuk et al. (2024) extended it to the multivariate case; and Luedtke et al. (2008) formalized the two-level latent variable framework.
- Version0.0.3.0
- R versionR (≥ 4.1.0)
- LicenseGPL-3
- Needs compilation?No
- Dashuk et al. (2024)
- Dashuk et al. (2024)
- Luedtke et al. (2008)
- Last release09/11/2025
Documentation
Team
Valerii Dashuk
MaintainerShow author detailsBinayak Timilsina
Show author detailsRolesAuthorMartin Hecht
Show author detailsRolesAuthorSteffen Zitzmann
Show author detailsRolesAuthor
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Dependencies
- Imports1 package
- Suggests3 packages