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hbsaems is an R package for area-level Hierarchical Bayesian Small Area Estimation (HBSAE). The methodological foundation follows the standard SAE literature – primarily Rao and Molina (2015) – while the computational implementation is adapted to the parameterisation and prior-specification conventions of the brms package (Bürkner, 2017), which targets the Stan back-end.
The package is designed to support the principled Bayesian workflow (Gelman and others, 2020) so that SAE modelling becomes systematic: prior predictive checks, MCMC convergence diagnostics, posterior predictive checks, leave-one-out cross-validation, Bayesian model comparison and averaging, prior sensitivity analysis, and design- consistent benchmarking are all part of the standard pipeline.
Scope. This package focuses on area-level SAE models. Unit- level SAE models (e.g. the nested error model of Battese, Harter & Fuller, 1988) are not the focus.
auxiliary, area_var,
spatial_var, spatial_model,
sampling_variance, n, deff).fixed_params mechanism for
power users and custom distributions.mice), or joint Bayesian imputation
(brms::mi()) with auto-selection.priorsense, design-consistent
benchmarking.prior_type.run_sae_app().You can install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("madsyair/hbsaems")Or with vignettes:
devtools::install_github("madsyair/hbsaems", build_vignettes = TRUE)library(hbsaems)
# Load example data
data("data_fhnorm")
str(data_fhnorm[, c("regency", "province", "y", "D", "x1", "x2")])
# Fit a basic Fay-Herriot Normal model with an IID area effect
model <- hbm(
formula = bf(y ~ x1 + x2 + x3),
hb_sampling = "gaussian",
hb_link = "identity",
re = ~(1 | regency),
data = data_fhnorm,
chains = 4, iter = 4000, warmup = 2000, cores = 2, seed = 1
)
# Check the model summary
summary(model) hbm()
Universal: any brms family, full customisation
â–˛
|
hbm_flex()
Family registry + auxiliary + fixed_params
â–˛
|
hbm_lnln() / hbm_betalogitnorm() / hbm_binlogitnorm()
SAE-friendly wrappers: response, auxiliary, area_var, ...Most users start with a wrapper. Step up to hbm_flex()
when you need a custom family or the generic fixed_params
interface; step up to hbm() when you need full brms
control.
hbm() – universal
modeling functionmodel <- hbm(
formula = bf(y ~ x1 + x2 + x3), # brms formula
hb_sampling = "gaussian", # likelihood family
hb_link = "identity", # link function
re = ~(1 | regency), # IID area RE (formula)
spatial_var = "province", # spatial RE column
spatial_model = "car", # "car" or "sar"
M = adjacency_matrix_car, # spatial weight matrix
data = data_fhnorm
)Beta logit-normal for proportions in \((0, 1)\):
fit_beta <- hbm_betalogitnorm(
response = "y",
auxiliary = c("x1", "x2", "x3"),
n = "n", # sample size column
deff = "deff", # design effect column
area_var = "regency", # area random effect
data = data_betalogitnorm,
chains = 4, iter = 4000, warmup = 2000, cores = 2, seed = 1
)Binomial logit-normal for successes out of trials:
fit_bin <- hbm_binlogitnorm(
response = "y",
trials = "n",
auxiliary = c("x1", "x2", "x3"),
area_var = "district", # 100 districts in this dataset
data = data_binlogitnorm,
chains = 4, iter = 4000, warmup = 2000, cores = 2, seed = 1
)Lognormal-lognormal (Fay-Herriot variant) for positive, right-skewed responses:
fit_lnln <- hbm_lnln(
response = "y_obs",
auxiliary = c("x1", "x2", "x3"),
area_var = "district",
sampling_variance = "psi_i", # known sampling variance (log scale)
data = data_lnln,
chains = 4, iter = 4000, warmup = 2000, cores = 2, seed = 1
)| Function | Purpose | Replaces (legacy) |
|---|---|---|
convergence_check() |
MCMC convergence diagnostics: \(\hat R\), ESS, Geweke, Heidelberger, Raftery | hbcc() |
model_compare() |
LOO/WAIC/Bayes-factor model comparison and posterior predictive checks | hbmc() |
model_compare_all() |
Multi-model ranking analogous to loo_compare |
– |
model_average() |
Bayesian model averaging (manual, stacking, or pseudo-BMA+ weights
via loo) |
– |
prior_check() |
Prior predictive checks | hbpc() |
prior_sensitivity() |
Power-scale prior sensitivity diagnostics
(priorsense) |
– |
sae_predict() |
In-sample and out-of-sample SAE prediction | hbsae() |
sae_benchmark() |
Design-consistent benchmarking | – |
| Function | Purpose |
|---|---|
check_data() |
Data integrity checks (missingness, duplicates, sample size) |
check_spatial_weight() |
Spatial weight matrix theoretical-compatibility checks |
build_spatial_weight() |
Build adjacency / row-standardised weight matrices |
is_converged() |
Quick yes/no convergence check |
posterior_interval() |
Credible intervals for posterior draws |
posterior_draws() |
Extract posterior draws as a long data frame |
hbm_info() |
Inspect model spec, priors, sampler settings |
hbm_warnings() |
Surface model fit warnings |
hbsaems ships with two custom brms families for
positive, right- skewed data:
flexsurv::dllogis and Wikipedia. See
?loglogistic and
?brms_custom_loglogistic.?shifted_loglogistic and
?brms_custom_shifted_loglogistic.Both are auto-registered with brms at package load via
register_hbsae_brms_custom().
The package ships a bilingual (English / Indonesian) Shiny application:
run_sae_app()The application provides a guided workflow: data upload → exploration → spatial setup → modelling → diagnostics → results download.
The package includes ten vignettes that progressively walk through the modelling workflow:
browseVignettes("hbsaems")| Vignette | Topic |
|---|---|
complete-workflow |
End-to-end Fay-Herriot example with full Bayesian workflow |
hbsaems-modelling |
Modelling concepts and the three-layer API |
hbsaems-betalogitnorm-model |
Beta logit-normal for proportions |
hbsaems-binlogitnorm-model |
Binomial logit-normal for counts |
hbsaems-lnln-model |
Lognormal-lognormal Fay-Herriot variant |
hbsaems-spatial |
CAR / SAR / BYM2 spatial models |
hbsaems-handle-missing |
Three missing-data strategies |
advanced-features |
Shrinkage priors, splines, custom families |
hbsaems-run_sae_app |
Using the Shiny application |
migration-guide |
Migrating from v0.x to v1.0.0 |
If you are coming from v0.x of hbsaems, see the
migration-guide vignette. The most visible changes:
| Old (deprecated) | New (canonical) |
|---|---|
hbcc() |
convergence_check() |
hbmc() |
model_compare() |
hbpc() |
prior_check() |
hbsae() |
sae_predict() |
group = |
area_var = |
sre = |
spatial_var = |
sre_type = |
spatial_model = |
predictors = |
auxiliary = |
sampling_var = |
sampling_variance = |
The old names continue to work in v1.0.0 with a deprecation warning and will be removed in v2.0.0.
If you use hbsaems in published research, please cite:
citation("hbsaems")GPL (>= 3)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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