Hierarchical Bayes Small Area Estimation Model using 'Stan'
hbcc : Hierarchical Bayesian Convergence Checks
Small Area Estimation using Hierarchical Bayesian under Beta Distribut...
Small Area Estimation using Hierarchical Bayesian under Logit-Normal M...
Small Area Estimation using Hierarchical Bayesian under Lognormal Dist...
hbm : Hierarchical Bayesian Small Area Models
hbmc: Check Model Goodness of Fit and Prior Sensitivity
hbpc : Hierarchical Bayesian Prior Predictive Checking
hbsae : Hierarchical Bayesian Small Area Estimation
Print a summary for a convergence check
Print a summary for a model goodness of fit and prior sensitivity
Print a summary for a fitted model represented by a hbmfit object
Print a summary for a prior predictive check
Print a summary for a prediction result
Launch the Shiny App for Small Area Estimation using Hierarchical Baye...
Create a summary of a convergence check
Create a summary of a model goodness of fit and prior sensitivity
Create a summary of a fitted model represented by a hbmfit object
Create a summary of a prior predictive check
Create a summary of a prediction result
update_hbm : Update a Hierarchical Bayesian Model (hbm) object
Implementing Hierarchical Bayesian Small Area Estimation models using the 'brms' package as the computational backend. The modeling framework follows the methodological foundations described in area-level models. This package is designed to facilitate a principled Bayesian workflow, enabling users to conduct prior predictive checks, model fitting, posterior predictive checks, model comparison, and sensitivity analysis in a coherent and reproducible manner. It supports flexible model specifications via 'brms' and promotes transparency in model development, aligned with the recommendations of modern Bayesian data analysis practices, implementing methods described in Rao and Molina (2015) <doi:10.1002/9781118735855>.