Package index
Model fitting (entry points)
Three layers of API for fitting a hierarchical Bayesian SAE model, from the most user-friendly distribution-specific wrappers to the universal brms interface, plus the post-fit refit helper.
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hbm() - hbm: Hierarchical Bayesian Small Area Models
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hbm_flex() - Fit a Flexible HBSAE Model with Any Registered Family
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hbm_lnln() - Small Area Estimation under a Lognormal-Lognormal Model
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hbm_betalogitnorm() - Small Area Estimation Under a Beta Likelihood (Logit-Normal Link)
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hbm_binlogitnorm() - Small Area Estimation under a Binomial Logit-Normal Model
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update_hbm() - Update a Fitted HBM
Pre-fit data & spatial-weight checking
Validate data structure, missing-data patterns, and adjacency matrices before fitting.
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check_data() - Inspect Data Before Fitting an HBSAE Model
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check_spatial_weight() - Validate a Spatial Weight Matrix Against CAR/SAR Theory
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build_spatial_weight() - build_spatial_weight: Construct M for CAR / SAR models
Configuration bundles
Helpers for assembling sampler / prior / nonlinear-term arguments into reusable named lists that can be passed to hbm() and its wrappers.
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hbm_control() - Sampler Configuration for HBSAE Models
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hbm_priors() - Prior Configuration for HBSAE Models
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hbm_nonlinear() - Nonlinear-Term Configuration for HBSAE Models
Bayesian workflow — diagnostics & comparison
Post-fit assessment of MCMC convergence, predictive performance, model selection, model averaging, and prior sensitivity. Together these cover stages 1, 3–7 of the canonical Bayesian workflow (Gelman et al. 2020).
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prior_check() - Prior Predictive Check for Fitted HBMs
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convergence_check() - MCMC Convergence Diagnostics for Fitted HBMs
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is_converged() - Test Whether a Fitted HBM Has Converged
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diagnostic_summary() - Extract a Diagnostic Summary
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model-compare - Compare Fitted HBMs
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model_compare() - Compare One or Two Fitted HBMs
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model_compare_all() - Compare Multiple Fitted HBMs
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model_average() - Bayesian Model Averaging on Small-Area Estimates
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prior_sensitivity() - Power-Scale Prior Sensitivity Diagnostics for Fitted HBMs
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is.hbsaems_check() - Test Whether an Object Is an hbsaems Check Result
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summary(<hbsaems_check>) - Generic Summary Method for hbsaems Check Results
Prediction & benchmarking
Out-of-sample prediction for unsampled areas (in-sample EBLUP and posterior predictive draws) and design-consistent benchmarking against direct estimates (Pfeffermann-style raking and ratio).
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sae_predict() - Generate Small Area Estimates
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sae_benchmark() - Benchmark Small-Area Estimates to Known Totals
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sae_aggregate() - Aggregate Predictions from Multiple hbsae_results
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sae_scale() - Standardise SAE Predictions
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sae_transform() - Apply a Transformation to SAE Predictions
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sae_filter() - Filter SAE Predictions by a Logical Condition
Posterior & prior extraction
Light-touch helpers for extracting draws, summaries, and credible intervals from hbmfit / brmsfit objects, including the prior-only fit produced by sample_prior = "only".
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posterior_draws() - Extract Posterior Draws as a Matrix
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posterior_interval(<hbmfit>) - Compute Credible Intervals for an hbmfit Object
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posterior_summary_hbm() - Comprehensive Posterior Summary
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prior_draws(<hbmfit>) - Extract Prior Draws
Custom brms families
Built-in custom distributions (loglogistic and shifted loglogistic) plus the framework for registering new families and Stan log-pdf functions through the model registry.
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brms_custom_loglogistic() - Loglogistic as a Custom Distribution Family for brms
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brms_custom_shifted_loglogistic() - Shifted Loglogistic as a Custom Distribution Family for brms
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dloglogistic()ploglogistic()qloglogistic()rloglogistic() - Loglogistic Distribution Functions
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dshifted_loglogistic()pshifted_loglogistic()qshifted_loglogistic()rshifted_loglogistic() - Shifted (3-Parameter) Loglogistic Distribution
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build_brms_custom_family() - Build a brms Custom Family + Stanvars Pair from a Single Spec
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read_stan_function() - Read the Stan Function Code for a Custom Distribution
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register_hbsae_brms_custom() - Register a brms Custom Family with the hbsaems Model Registry
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register_hbsae_model() - Register a Custom HBSAE Model
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list_hbsae_models() - List Registered HBSAE Models
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get_hbsae_model() - Inspect a Registered HBSAE Model Specification
Object methods & internal state
S3 constructors, validators, and methods for the hbmfit class along with helpers to inspect a fitted model’s metadata, raw data, and any warnings raised during fitting.
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hbmfit() - User-Facing Helper to Build an hbmfit Object
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new_hbmfit() - Create a New hbmfit Object
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validate_hbmfit() - Validate an hbmfit Object
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hbmfit-class - The hbmfit S3 Class
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hbm_data() - Return the Data Used to Fit an hbmfit
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hbm-info - Model Inspection Helpers
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hbm_info() - Get Comprehensive Model Information
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hbm_warnings() - Get Model Warnings
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is.hbmfit()is.hbcc_results()is.hbmc_results()is.hbpc_results()is.hbsae_results() - Test Whether an Object Belongs to an hbsaems Result Class
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hbmfit-methods - Standard S3 Methods for hbmfit
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posterior-methods - Posterior and Prior Extraction Methods for hbmfit
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plot(<hbmfit>) - Plot a Fitted hbmfit Object
Shiny dashboard
Launch the interactive bilingual (English / Indonesian) SAE GUI and inspect its translation infrastructure.
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run_sae_app() - run_sae_app: Interactive Small Area Estimation Application
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check_shiny_deps() - Check Shiny App Dependencies
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tr() - Translate a UI String for the Shiny SAE App
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tr_langs() - List Available Languages
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tr_keys() - List All Translation Keys (for a Reference Language)
Datasets
Example datasets shipped for vignettes and tests, including spatial adjacency matrices used by the CAR / SAR / BYM2 examples.
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data_fhnorm - Simulated Fay-Herriot Normal Data
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data_lnln - Simulated Lognormal-Lognormal Data
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data_betalogitnorm - Simulated Beta Logit-Normal Data
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data_binlogitnorm - Simulated Binomial Logit-Normal Data
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adjacency_matrix_car - Province-level Adjacency Matrix
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adjacency_matrix_car_regency - Regency-level Adjacency Matrix (Coarse Spatial Cluster)
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spatial_weight_sar - Spatial Weight Matrix for Simultaneous Autoregressive Models