hbsaems 0.1.0

• Initial GitHub submission.

• Added the main function hbm() for general hierarchical Bayesian modeling in the context of Small Area Estimation (SAE).

• Added model-specific functions:

  • hbm_beta() for Beta distribution modeling.
  • hbm_logitnormal() for Logit-Normal distribution modeling.
  • hbm_lognormal() for Log-Normal distribution modeling.

• Added model diagnostic functions:

  • hbcc() for convergence checking (e.g., using trace plots, Rhat, and effective sample size).
  • hbmc() for evaluating model goodness-of-fit.

• Added hbsae() function for producing area-level predictions and estimates based on fitted models.

• Added run_sae_app() to launch an interactive Shiny application for upload data, model specification, fitting, checking, and result exploration.

hbsaems 0.1.1

• Updated hbmc():
  • Added handling for problematic k Pareto values in LOO diagnostics.
  • Included prior sensitivity analysis as part of the model checking process.
• Improved hbsae():
  • Replaced posterior_predict() with posterior_epred() from the brms package for better compatibility and interpretation.
  • Added computation of Mean Squared Error (MSE) for predicted estimates.
  • Removed the scaling option for prediction; SAE predictions are now standardized using posterior_epred() consistently.
• Updated model-specific functions (hbm_beta(), hbm_logitnormal(), and hbm_lognormal()):
  • Weakly informative priors are now used by default for all regression coefficients (betas), enhancing model stability while preserving flexibility.
• Shiny App Enhancements via run_sae_app():
  • Added Update Model menu for re-fitting existing models with new data or configurations.
  • Added Explore Data tab for visual and tabular examination of uploaded datasets.
  • Integrated Prior Sensitivity Analysis tab for evaluating the impact of prior assumptions on model results.
  • Improved posterior predictive plot and download features.