NEWS.md
In hbsaems: Hierarchical Bayes Small Area Estimation Model using 'Stan'

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.

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.