MultiLevelOptimalBayes
0.0.4.0Regularized Bayesian Estimator for Two-Level Latent Variable Models
Overview
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. (2025a) doi:10.1017/psy.2025.10045 derived the optimal regularized Bayesian estimator; Dashuk et al. (2025b) doi:10.1007/s41237-025-00264-7 extended it to the multivariate case; and Luedtke et al. (2008) doi:10.1037/a0012869 formalized the two-level latent variable framework.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 50%
- References docs
- 6%
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Package metadata
- First published
- 2025-05-27
- Total releases
- 5 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 47 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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