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MultiLevelOptimalBayes  

Regularized Bayesian Estimator for Two-Level Latent Variable Models
View on CRAN: Click here


Download and install MultiLevelOptimalBayes package within the R console
Install from CRAN:
install.packages("MultiLevelOptimalBayes")

Install from Github:
library("remotes")
install_github("cran/MultiLevelOptimalBayes")

Install by package version:
library("remotes")
install_version("MultiLevelOptimalBayes", "0.0.1.6")



Attach the package and use:
library("MultiLevelOptimalBayes")
Maintained by
Valerii Dashuk
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-05-27
Latest Update: 2025-05-27
Description:
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.
How to cite:
Valerii Dashuk (2025). MultiLevelOptimalBayes: Regularized Bayesian Estimator for Two-Level Latent Variable Models. R package version 0.0.1.6, https://cran.r-project.org/web/packages/MultiLevelOptimalBayes. Accessed 12 Jun. 2025.
Previous versions and publish date:
0.0.1.5 (2025-05-27 10:40)
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