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Minor release with Bayesian-workflow improvements. Read the behaviour change below before upgrading production scripts.
sae_predict() now defaults to
predict_type = "epred" (the posterior distribution
of the area mean theta_i), replacing the previous
posterior-predictive-of-a-new-observation behaviour. This is the
theoretically correct target for small area estimation: the reported
SD / RSE_percent now measure the uncertainty
of the estimator theta_i and are therefore
smaller than in 1.0.x (which included observation-level
likelihood variance). Every RSE number your scripts produce will
change. To reproduce the old numbers exactly, pass
predict_type = "response". A third option,
"linpred", returns the linear predictor on the response
scale.Four SAE-oriented families added to the model registry, usable
via hbm_flex() / hbm() and
list_hbsae_models(): gamma
(positive continuous, log link – incomes/expenditures),
skew_normal (skewed continuous area
means), student (heavy-tailed, robust
Fay-Herriot), and hurdle_lognormal
(positive continuous with excess zeros). Each carries the appropriate
response-domain check (e.g. Gamma requires y > 0; hurdle-lognormal
allows y >= 0).
hbm_flex()’s family argument is now the
primary way to choose the family. It accepts a string
(e.g. "gaussian", exactly like the old
family_key), a brms family object
(e.g. gaussian(), binomial()), or a registered
custom family object (e.g.
brms_custom_loglogistic()$custom_family). The family name
is matched to a registered spec, so custom distributions reuse the same
Stan code (stanvars); an unregistered custom family raises an
informative error. The family_key argument is retained as a
backward-compatible alias (not deprecated). Supply either
family or family_key, not both.
hbm() gains a family argument mirroring
hbm_flex(): pass a string (e.g. "gaussian") or
a brms family object (e.g. gaussian(),
Gamma(link = "log")); a link carried on the object is
honoured when hb_link is left at its default.
hb_sampling is retained as a backward-compatible alias.
Supply either family or hb_sampling, not both.
family is thus the uniform distribution selector across
hbm() and hbm_flex().
sae_predict() gains the
predict_type = c("epred", "response", "linpred", "proportion")
argument (see above). model_average() and
sae_benchmark() honour it. For a binomial
family, posterior_epred() returns the expected COUNT (n_i *
p_i), which is not comparable across areas with unequal trial counts;
predict_type = "proportion" returns the area proportion
p_i, and predict_type = "epred" on a binomial model now
auto-converts to p_i (with a warning). Use "response" for
the count.
convergence_check() now reports divergent
transitions, E-BFMI per chain, and the
max-treedepth hit rate as numbers in
summary() (not just the energy plot), and warns when
divergences occur or E-BFMI < 0.3.
model_compare() / model_compare_all()
now check Pareto-k for PSIS-LOO reliability: a
n_high_k column is added to the ranking table and a warning
is issued when any k > 0.7. The previously dead
reloo_args argument is now functional – supplying it
triggers brms::reloo() to refit the high-k folds.
hbm() now defaults to
control = list(adapt_delta = 0.95, max_treedepth = 12)
(the values long documented as the recommended starting point) instead
of brms’s adapt_delta = 0.8. Hierarchical SAE funnels need
the higher value to avoid spurious divergences. User-supplied
control values are always respected. Seed-level results may
differ slightly from 1.0.x.convergence_check() no longer reports
Tail_ESS = Bulk_ESS in the fallback path when the
‘posterior’ package is unavailable; it reports NA rather
than falsely implying the tails were checked.model_compare() (and the deprecated
hbmc()) accept a list of fitted models again. When
model_compare() became an S3 generic in 1.0.0 it dispatched
only on hbmfit/brmsfit, so the pre-1.0.0
convention hbmc(model = list(m1, m2)) failed with “no
applicable method for ‘model_compare’ applied to an object of class
‘list’”. A model_compare.list() method now unwraps a 1-2
element list to the single/pairwise path (hbmc_results),
routes a 3+ element list to model_compare_all() (ranked
hbm_table), and rejects an empty or non-model list with an
informative error.First stable release. hbsaems fits
area-level Hierarchical Bayesian Small Area Estimation
models following the methodological tradition of Rao and Molina (2015)
doi:10.1002/9781118735855, with computational
implementation adapted to the parameterisation and prior-specification
conventions of the ‘brms’ package (Buerkner 2017 doi:10.18637/jss.v080.i01) and Stan back-end. The
package is designed to support the principled Bayesian workflow
advocated by Gelman et al. (2020) doi:10.48550/arXiv.2011.01808 – 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.
This entry consolidates the changelog for every development cycle since the 0.1.2 maintenance release; the package now follows semantic versioning and the 1.0.0 line constitutes a stable user-facing function set whose signatures will not change before v2.0.0.
Shiny app: memory management for multi-model comparison. The dashboard’s “snapshot library” lets users compare or average predictions across several fitted models in one session. Each snapshot is a full object (50-200 MB for a typical Fay-Herriot fit), so a session that snapshots 5+ models could easily blow past 1 GB of RAM. Three composable mitigations ship in v1.0.0: The multi-model panel now also shows a per-snapshot size-in-MB readout below the comparison buttons, so users can see exactly how much memory each snapshot is costing.
Shiny app now exposes the Fay-Herriot
sampling_variance argument. The interactive
run_sae_app() previously offered the survey-design route to
fixing \(\phi_i\) in the
Beta-Logitnormal workflow (via n + deff) but did NOT expose
the equivalent sampling_variance = "psi_i" slot for the
Lognormal-Lognormal workflow (where it pins \(\sigma_i = \sqrt{\psi_i}\)) nor for the
Custom (Gaussian / Lognormal / Student) workflow. Both gaps have been
closed: a new “Sampling Variance (psi_i)” dropdown appears on the
Lognormal-Lognormal panel and a “Sampling Variance (D_i)” dropdown
appears under the Custom panel when the user picks a family that
supports it. Selecting a column wires it straight through to
hbm_lnln() / hbm(), which then apply the
link-override fix from this release transparently. This means the
classical Fay-Herriot SAE workflow is now end-to-end usable from the GUI
without dropping back to scripted calls.
Shiny app exposes measurement_error sugar
(Ybarra-Lohr 2008). A new collapsible “Measurement-error
covariates” box on the Modeling tab lets the user mark one auxiliary
covariate as noisily-measured and supply the column holding its standard
error. hbsaems then rewrites into transparently.
Shiny app exposes the generic fixed_params
slot. A new collapsible “Fixed distributional parameters
(advanced)” box on the Modeling tab accepts an R expression evaluating
to a named list (e.g. or ). Power users can now pin any distributional
parameter (sigma, phi, shape, nu, …) from the GUI without writing
scripts.
Shiny app exposes the sae_* post-processing
helpers. A new “Post-processing” sub-tab under Results lets the
user apply (log / exp), (centre + std, or centre only), and
(RSE-threshold cutoff) to the most recent prediction and download the
transformed table.
Shiny app exposes multi-model workflows. A new “Multi-model” sub-tab under Results provides an in-app library of named model snapshots. The user clicks “Snapshot current fit” to save a copy of under a custom name, then selects 2+ snapshots to feed into (LOO/WAIC) or (Bayesian model averaging). This closes the long-standing gap between the scripted multi-model API and the GUI workflow.
Generic fixed_params mechanism in
hbm() and hbm_flex(). Lets the user pin any
distributional parameter to a value derived from a column, scalar,
vector, or formula evaluated against the data. Centralises a pattern
that previously had to be coded per-wrapper.
hbm_betalogitnorm() refactor. Four
operational modes: random \(\phi\) with
hyperprior; fixed \(\phi\) from survey
\(n\) and \(\mathrm{deff}\); user-overridable
hyperprior on \(\alpha,\beta\) via
stanvars; and generic fixed_params for power
users. Default priors filled in automatically and follow Liu
(2009).
hbm_lnln(sampling_variance = ...)
for the Fay–Herriot lognormal model. Pins \(\sigma_i = \sqrt{\psi_i}\) from a known
per-area sampling variance.
Custom brms response distributions. Built-in
support for Loglogistic and Shifted Loglogistic, plus a public extension
framework (register_hbsae_brms_custom(),
read_stan_function(),
build_brms_custom_family()). Stan code lives in
inst/stan/ as plain .stan files. Each
registered family ships log-likelihood, posterior-predict and
posterior-epred hooks so loo(),
posterior_predict(), and posterior_epred()
work out of the box.
Spatial random effects. CAR (ICAR / proper /
BYM2) and SAR (lag / error) via spatial_var,
spatial_model, car_type,
sar_type, and M. Weight matrices can be
constructed with the bundled build_spatial_weight() from a
shapefile or coordinates.
Missing-data handling. Three strategies
(deleted, multiple via mice,
model via brms::mi()) with auto-selection when
handle_missing = NULL.
Shrinkage priors. Horseshoe (regularised, Piironen & Vehtari
prior_type.Nonlinear smooth terms with full brms-canonical API. Penalised regression splines via and Gaussian processes via :
spline_k (basis dim) and
spline_bs (basis type: "tp",
"cr", "cs", "ps").gp_k (Hilbert-space approximate GP basis dimension
– Riutort-Mayol et al. 2023), gp_cov (covariance function:
"exp_quad", "matern15",
"matern25", "exponential"), gp_c
(boundary-scale factor).gp_k value.gp_scale deprecated in favour of gp_c;
removal scheduled for v2.0.0.Bilingual Shiny dashboard
(run_sae_app()). English / Indonesian, dedicated spatial
setup tab, in-app code preview, and CSV / RDS data upload. Source under
inst/shiny/sae_app/.
Benchmarking helpers
(sae_benchmark(), sae_predict()).
Pfeffermann-style design-consistent benchmarking and out-of-sample
prediction for unsampled areas.
Bayesian model averaging with LOO weights.
model_average() now accepts a method argument:
"manual" (default, user weights), "stacking",
or "pseudobma" (both via
loo::loo_model_weights, the canonical Bayesian stacking /
pseudo-BMA+ of Yao et al. 2018).
Power-scale prior sensitivity diagnostics via
prior_sensitivity(): thin wrapper around
priorsense::powerscale_sensitivity() (Kallioinen et
al. 2024) for detecting prior-data conflict and weak likelihood in
fitted models.
Hierarchical area_var for multi-stage
SAE. hbm_flex(), hbm_lnln(),
hbm_betalogitnorm(), and hbm_binlogitnorm()
now accept area_var as a character vector (highest level
first, e.g. c("province", "regency")) and a companion
area_re_structure argument that selects between
"nested" (default; produces
(1 | province / regency)) and "crossed" random
intercepts. Length-1 input behaves exactly as in earlier releases, so
existing code continues to work unchanged.
Custom Stan family name prefix
hbsae_. To avoid a symbol collision with Stan’s
built-in loglogistic_lpdf (Stan >= 2.29), the Stan
function definitions for the loglogistic and shifted loglogistic
families are now named hbsae_loglogistic_lpdf /
hbsae_shifted_loglogistic_lpdf and live in
inst/stan/hbsae_loglogistic.stan /
inst/stan/hbsae_shifted_loglogistic.stan. The user-facing R
helpers (dloglogistic,
brms_custom_loglogistic, etc.) and the registry keys
("loglogistic", "shifted_loglogistic") are
unchanged.
hbm() now accepts
sampling_variance = "<col>" as the
Fay-Herriot sugar (previously only available in
hbm_lnln()). Pins via offset and is the canonical way to
fit a Gaussian Fay-Herriot model. Without it the residual and the
area-RE compete to explain the same variance, producing weak
identifiability and divergent transitions almost regardless of
adapt_delta. All vignettes using data_fhnorm
have been updated accordingly.
Family compatibility check:
sampling_variance is only valid for continuous families
that expose a residual SD parameter named sigma (gaussian,
lognormal, student, skew_normal, exgaussian, asym_laplace). Passing it
with Beta / Binomial / Poisson / Gamma / Weibull families now raises an
explicit error pointing the user at the appropriate family-specific
mechanism (e.g.
fixed_params$phi for Beta via design effect,
trials for Binomial).
Centralised sugar-to-fixed_params
translation. The previously duplicated translation logic in
hbm(), hbm_lnln(), and
hbm_betalogitnorm() is now consolidated in two internal
helpers (, ) with consistent validation, conflict checks against
fixed_params, and error messages. Behaviour is unchanged
from the user’s point of view; the refactor eliminates ~60 lines of
duplicated code.
data_fhnorm regenerated with a
deterministic, well-identified simulation
(set.seed(20260518L)). Covariates are standardised to ,
sigma_u = 1.0, and so that vignettes fit cleanly with default brms /
Stan settings. See data-raw/data_fhnorm.R for the
reproducible generator.
measurement_error sugar (Ybarra and
Lohr 2008). hbm() and the hbm_* wrappers
accept which rewrites the brmsformula on the fly to wrap the listed
auxiliary variables with . Validation enforces non-negative, NA-free
standard errors and that the named variables are part of
auxiliary.
Automatic mi() / me() detection.
When the user writes or explicitly in the formula, hbm() no
longer demands handle_missing be set and no longer drops
rows with NA: brms’s joint-modelling / measurement-error
framework handles the imputation internally. Internally
handle_missing is silently set to "model" in
that case.
Conditional link_phi resolution in
hbm_betalogitnorm(). Default is now
NULL and resolves automatically: "identity"
when phi is pinned via fixed_params$phi (the
survey-design mode), "log" (brms default) when
phi is estimated via a hyperprior. This eliminates a class
of divergent transitions caused by NUTS proposing negative on the
identity scale. Manually setting "identity" in random mode
emits a warning.
Simplified phi prior in
hbm_betalogitnorm() random mode. The pre-existing
hierarchical construction has been replaced by brms’s own default (lower
bound 0). The wrapper no longer declares or as Stan parameters and no
longer injects sampling statements for them. Rationale: the prior on
(declared as ) was on the boundary of its support, producing divergent
transitions on weakly-informative data; the extra layer also inflated
the effective posterior dimension for what is essentially one scalar
parameter per area model. users who relied on the old construction can
build it manually via the + arguments; see for the migration note.
Legacy code that still passes containing sampling statements on / now
raises an informative error pointing the user at the new
mechanism.
sae_benchmark() defaults made semantically
explicit. New argument
target_type = c("total", "mean") (default
"total") is consulted only when
weights = NULL, choosing a safe default weighting
(rep(1, n) for total, rep(1 / n, n) for mean).
An informational message is emitted so the chosen weighting is always
visible. Production users should still pass explicit
weights = N_i (population size per area).
predictors ->
auxiliary. The new name aligns with Small Area
Estimation literature (Rao & Molina 2015, Pfeffermann 2013). Old
usage continues to work with a one-time soft-deprecation warning and is
scheduled for removal in v2.0.0.hbm_generic() ->
hbm_flex(). Old name removed.hbcc(), hbmc(), hbpc(),
hbsae(). All four emit a soft-deprecation warning pointing
at hbm(), convergence_check(),
prior_check(), sae_aggregate(), or
sae_predict() respectively. Scheduled for removal
in v2.0.0.hbsaems-modelling (overview),
hbsaems-lnln-model,
hbsaems-betalogitnorm-model,
hbsaems-binlogitnorm-model, hbsaems-spatial,
hbsaems-handle-missing, hbsaems-run_sae_app.
All follow a CRAN-safe pattern: heavy Stan fits are not evaluated at
build time; representative outputs are printed as illustrations with an
explicit disclaimer.complete-workflow, advanced-features,
migration-guide.hbsaems-betalogitnorm-model vignette now documents two
modes for \(\phi\) (Random and Fixed)
instead of three, and includes a legacy-reproduction recipe for users
who need the pre-v1.0.0 hierarchical hyperprior. The
migration-guide vignette gained two new sections covering
the phi prior simplification and the critical link function override
fix. The hbsaems-lnln-model vignette adds an implementation
note explaining why the sampling_variance offset is now
interpreted on the natural (untransformed) scale.CRAN vignette now carries real output. The
complete-workflow vignette previously used
knitr::opts_chunk$set(eval = FALSE) globally and showed all
R code as display-only. This kept CRAN’s vignette build short but also
meant readers could not see what any of the seven workflow steps
actually produces.
The revised vignette evaluates a curated subset of chunks:
The production-grade hbm(..., chains = 4, iter = 4000)
calls for the prior-predictive check, the headline model fit, the
model-comparison, and model-averaging are kept as display-only blocks
(lowercase, not evaluated by knitr) – those would push the CRAN vignette
build well past its time budget. The text now explicitly tells readers
to use those full settings in their own analyses, not the toy
iter = 200 of the demo.
A Stan-toolchain probe in the setup chunk silently falls back to display-only mode when Boost / a C++ compiler is unavailable (e.g. on a minimal CI container), so the vignette renders cleanly everywhere – it just has fewer outputs on Stan-less systems.
GitHub Actions: automated pkgdown deployment.
Three workflows live in .github/workflows/: A new at the
repository root explains first-time setup (GitHub Pages settings,
Actions write permissions, URL field in DESCRIPTION) and the local
pkgdown::build_site() preview workflow. The guide is Rbuildignored so it
does not bloat the CRAN tarball.
Vignette strategy reorganised for the CRAN
release. Through v1.0.0 development we built up an extensive
collection of vignettes covering every distribution-specific wrapper,
every advanced topic, and the internals. Eleven vignettes was a great
documentation set but a heavy load for a CRAN tarball; CRAN’s guidance
for new submissions favours a small set of essential vignettes. We now
ship only one vignette in the tarball,
complete-workflow, which is the canonical end-to-end SAE
pipeline using hbm() and the standard Bayesian workflow
diagnostics. The remaining ten files are kept in the source repository
under vignettes/articles/ (a pkgdown convention) and are
rendered as articles on the package website at https://madsyair.github.io/hbsaems/. R CMD build skips
vignettes/articles/ via .Rbuildignore, so the
CRAN tarball is leaner (-46 KB) without losing any documentation – users
who install from CRAN still get the website link via the
URL: field in DESCRIPTION.
New article: “AST-based Formula Manipulation”. A
walkthrough of how hbsaems rewrites user formulas
internally to apply the nonlinear,
measurement_error, area_var,
sampling_variance, and handle_missing sugar –
and why a regex-based approach would silently corrupt many legitimate
formulas. Available at https://madsyair.github.io/hbsaems/articles/ast-formula-manipulation.html.
sae_predict(model, newdata = X) failed with
“variables can neither be found in ‘data’ nor in ‘data2’” when the model
was fit with sampling_variance = or
fixed_params =. Those sugar arguments inject
internal offset columns named (e.g. for the Fay-Herriot construction)
into the training data, and the brms formula then carries . A user
passing a fresh that did not carry those columns hit a cryptic
error.
now repopulates the offset columns automatically when the user-supplied has the same number of rows as the training data (the typical case of “predict at the same areas, possibly with updated covariates”). When differs, sae_predict() raises an informative error telling the user either to align the row count or to compute the offset column themselves (e.g. ).
The same fix benefits , which internally calls for each candidate model.
Seven regression tests cover the in-place copy path, the pass-through paths (no offset cols, or already populated by user), the nrow-mismatch error, and the NULL-training-data guard.
GitHub Actions R-CMD-check --as-cran failed
with “Boost not found” on Linux runners. CRAN’s build farm
ships the full Stan toolchain (BH / RcppEigen / RcppParallel /
StanHeaders) by default, but GitHub’s minimal R container does not – and
the r-lib/actions/setup-r-dependencies@v2 step had no way
to know which transitive C++ header packages our
\donttest{} examples needed. All three workflows
(R-CMD-check.yaml, vignettes.yaml,
pkgdown.yaml) now declare these packages explicitly via
extra-packages:, so the rstan compile path works.
MCMC settings in \donttest{} examples
standardised to brms defaults. Examples now consistently use
chains = 4, iter = 2000, warmup = 1000 (the brms defaults
documented in ?brms::brm). Each example block also carries
a brief comment noting that production-grade inference on tougher
posteriors (funnel geometry, weakly identified priors) may require
iter = 4000, warmup = 2000 and
control = list(adapt_delta = 0.99), with cross-references
to the complete-workflow vignette.
GitHub Actions R-CMD-check --as-cran failed
with “Boost not found” on Linux runners. CRAN’s build farm
ships the full Stan toolchain (BH / RcppEigen / RcppParallel /
StanHeaders) by default, but GitHub’s minimal R container does not – and
the r-lib/actions/setup-r-dependencies@v2 step had no way
to know which transitive C++ header packages our
\donttest{} examples needed. All three workflows
(R-CMD-check.yaml, vignettes.yaml,
pkgdown.yaml) now declare these packages explicitly via
extra-packages:, so the rstan compile path works.
MCMC sampling in \donttest{} examples was
unnecessarily heavy. Many examples ran
chains = 2, iter = 2000 (some
chains = 4, iter = 4000), which added up to several minutes
of example-run time during R CMD check –run-donttest. CRAN’s guidance is
that examples should run in a few seconds total per function. All such
examples have been retuned to
chains = 1, iter = 500, warmup = 250 (a few seconds each)
and prefixed with a comment explaining that production settings should
be used in real analyses. The displayed code in vignettes still
references the full production settings.
vignettes/articles/hbsaems-betalogitnorm-model.Rmd
showed a legacy code block that no longer runs. The article had
a “Reproducing the legacy hierarchical hyperprior” section that
demonstrated the pre-v1.0.0 construction by calling . As of v1.0.0,
explicitly rejects that pattern at construction time with an informative
error (/ are no longer declared as Stan parameters by the wrapper).
Users who copy-pasted the article’s example would have hit the runtime
error.
The section has been rewritten: The migration-guide article has been updated to cross-reference the new corrected example.
Article YAML headers used
rmarkdown::html_vignette. Files under are
pkgdown-only and no longer carry a \\VignetteIndexEntry{}
field (that field is what relies on for the page title). Manually
rendering these files with would emit a benign warning about the missing
field. We now use for articles, which works identically when rendered by
pkgdown but is silent under direct too – helpful for contributors
previewing changes locally.
?register_hbsae_brms_custom example was
non-idempotent. The example registered every time it ran but
never cleaned up. Running the example a second time in the same R
session (which R CMD check’s example runner can do, and which
interactive users routinely do) would error with . The example now
removes any pre-existing entry before registering and unregisters at the
end, so re-runs are clean.
subdirectory removed. The file (31 KB) shipped with the package because it lived under . Its content was redundant with the curated articles on the package website and bloated the CRAN tarball. Also removed three internal maintainer documents that had drifted into : (duplicate of the root version), (internal note), and (moved to repository root and Rbuildignored). Only the runtime-essential , , and remain. Net reduction in installed package size: ~50 KB.
?hbm_betalogitnorm example 3 used the
removed alpha/beta hyperprior pattern. R CMD check caught a
leftover example block that attempted to declare priors on and via ,
both of which were Stan parameters in the pre-v1.0.0 hierarchical phi
construction but were removed in this release (see the migration note at
the top of ). Example 3 has been rewritten to demonstrate the supported
v1.0.0 pattern – a non-default phi prior is passed via brms’s standard
rather than via the legacy stanvars sampling statements. The resulting
Stan code uses as intended.
R CMD check NOTE: tests/spelling.Rout.save
comparison. R CMD check was diffing the live spelling test
output against a saved snapshot, which broke any time a new acceptable
term appeared in NEWS or vignettes (e.g. from an internal predictions
data structure). We have removed the comparison file and rely solely on
, which uses the proper mechanism. This is the approach recommended by
Jeroen Ooms (author of ) for packages that want spelling checks during
development but not on CRAN’s machines.
AST-based formula rewriting silently dropped
offset() terms. Both (used by the sugar) and (used
by the sugar) decompose the formula RHS via and reassemble it with . The
reassembly step iterated only over , which is the list of predictor
terms – those live in a separate attribute as integer positions into .
As a result, a user formula like called with would silently lose the
offset, producing . Both helpers now extract the offset terms and splice
them back into the assembled RHS verbatim. Eight regression tests cover
offset preservation, substring-name safety, wrapper preservation (I,
poly, me), interaction-term preservation, and the hierarchical
.
Conflict protection: custom brms families could silently shadow built-in or brms-native families. Three classes of conflict were previously possible:
This release introduces a comprehensive list that covers both brms-native AND hbsaems-bundled custom families, makes the override-protection logic in both and consult it, adds a probe so registrations that would shadow a brms-native family emit an informative warning pointing the user at the prefix convention, and rewrites to clear non-builtin keys before re-asserting the built-ins.
An internal option () lets bypass the new check during package load, avoiding a chicken-and-egg circular dependency.
Six regression tests cover all paths.
dloglogistic() errored when mu
or beta was NA. treats as failure, so stopped with
. Now NAs in parameters propagate as NA in the result (matching the
v1.0.0 NA-in-x fix and the wider R density-function convention), while
finite non-positive parameters still error explicitly.
Stan function names safely prefixed with
hbsae_. Audited and to confirm every Stan-side
function declaration uses the prefix, avoiding collision with Stan
2.29+’s built-in . This was already correct in the code; the audit added
documentation comments and a verifying dev-test.
dloglogistic() silently coerced NA inputs to
0. Base R density functions follow the convention that , but
our returned 0 (the density at , which is also 0). The vectorised case
was particularly misleading because a partial NA vector produced no
warning even though one position was being silently replaced with 0. We
now propagate NA from any of , , or – matching the behaviour and the
wider R density- function convention.
run_sae_app() produced a cryptic error when
was missing. Because lives in , users who installed hbsaems
with only the modelling dependencies would see “could not find function
runApp” rather than an actionable error. We now check at the top of and
emit an informative error pointing to .
run_sae_app(check_deps = FALSE) carried
stale optional_missing state across sessions. If
the user previously launched with (which writes ) and then re-launched
with , the GUI banner would still display the cached missing-package
list. We now clear the option to in the branch.
DESCRIPTION Suggests: missing
energy and minerva. The Shiny app’s
exploratory-correlation panel (Distance correlation, MIC) depends on
these two packages, but was the only place that referenced them. Without
listing them in Suggests, downstream automated checks (e.g. ) would have
flagged the unconditional call as a NOTE. Both packages are now in
Suggests as documented in .
Bundled data: HIERARCHICAL CONSISTENCY across
datasets. All four bundled datasets now use the same
administrative-level labels with the same meanings: What differs between
datasets is the analysis resolution – i.e.
at which level the 100 small-area observations live: Label format
conventions disambiguate at a glance: three-digit suffixes (, ) for the
100-level fine analysis areas and two-digit suffixes (, ) for the
5-level coarse spatial clusters. Through earlier v1.0.0 iterations the
same role was variously called “regency” (creating dual-semantics
confusion), “kabupaten” (Indonesian-only), and “county” (US-only); none
of those reached CRAN. The matrix has been correspondingly renamed
adjacency_matrix_car_regency (5x5, rownames ). Total impact
on data folder vs original pre-audit state is +4 bytes
(29822 -> 29826) – effectively neutral.
Documentation: incorrect spatial pairings in three
examples. Two SAR examples paired
spatial_weight_sar (100x100, regency-named) with
spatial_var = "province" (5 levels) – this would error at
fit time because brms cannot resolve a 5-level grouping factor against a
100-row weight matrix. One BYM2 example paired
data_binlogitnorm (which has no province
column) with spatial_var = "province". All three examples
in ?hbm, vignette('hbsaems-spatial'), and
inline documentation have been corrected.
print.hbcc_results() reported PASS on all-NA
Rhat. Same pattern as the fix, but in the print method: returns
TRUE on and silently displayed for degenerate fits with no finite Rhat
values. Now reports .
print.hbsae_results() printed
NaN% and Inf to -Inf. When is NA /
NaN (all areas had zero predictions – see the v1.0.0 RSE guard in ) the
print method would output literal \code{“NaN %”} and for the prediction
range. Both are now handled with explicit branches that label them as
.
Bundled-data integrity now checked at every test run. A new test file verifies that: These tests would have caught the historical “regency means 100 levels in two datasets and 5 levels in two others” mismatch before it shipped.
hbm_warnings() failed to flag degenerate
model fits. The R-hat convergence check used , which evaluates
to (i.e. no warning fires) when all Rhat values are NA – the textbook
signature of a sampler that produced no useful draws. Same issue
affected the check. Both now detect the all-NA / all-non- finite case
explicitly and emit a dedicated warning.
register_hbsae_model() silently overwrote
built-in families. Passing for a built-in key (, , , , etc.)
silently replaced the package-curated spec, losing the validation rules
and link handling that the built-in provides. Without the function now
refuses to touch a built-in; with it emits a warning so the choice is
conscious.
register_hbsae_model() accepted invalid
registry keys. Keys with whitespace (), leading digits (), or
other punctuation that would force backtick-quoting are not usable as R
names and broke downstream registry access. Keys are now validated with
. Empty-string keys are also rejected.
register_hbsae_model() did not validate
type. Non-character or vector-of- strings values would either
error cryptically downstream or produce garbled messages. Now validated
as or a single character string.
register_hbsae_brms_custom() had thinner
validation than its sibling. The brms-custom-family
registration path used a bare for the key, didn’t validate , , , or ,
and let callers silently shadow built-in families. Validation now
matches .
is_converged() returned silent TRUE on
all-NA Rhat. Both S3 methods relied on , which returns TRUE for
after the NA’s are stripped – producing a degenerate “converged” signal
whenever the underlying brmsfit had no finite Rhat values (e.g. a
collapsed sampler, all chains diverged, mock object). now returns with
an informative warning in that case.
is_converged() did not validate the
threshold argument. A string threshold (), ,
length>1 vector, negative value, or non-finite value all silently
returned TRUE due to R’s coercion rules in and . We now reject those
inputs explicitly via .
sae_benchmark() silently produced NA / Inf
when target was non-finite. Passing or propagated
through the ratio / difference computation and produced an all-NA /
all-Inf benchmarked table. Both are now rejected with informative
errors. Likewise, non-finite values in now raise an early error pointing
the user back to the model-fit step.
Legacy callback removed from the Beta family registry. The pre-v1.0.0 hierarchical hyperprior \(\phi \sim \mathrm{Gamma}(\alpha, \beta)\) with \(\alpha, \beta \sim \mathrm{Gamma}(1, 1)\) was previously hard-wired in the registry under . That made silently re-inject the old construction even after was simplified to use brms’s default prior in . The callback has been removed from the built-in spec; custom families can still register one via .
hbm_flex() length-1 area_var validation
crashed on length>1 vectors. When the user supplied an
area_var character vector of length 2 or more (the
hierarchical-area mode), the validation
!is.null(area_var) && !(area_var %in% names(data))
raised the R 4.2+ error – the && operator does not
coerce length>1 logicals to scalar. Replaced with a
setdiff()-based check that produces an informative error
listing every missing column. Same idiom applied defensively to
spatial_var (which is always length-1 by API
design).
re = ~ (1 | x/y) nested syntax rejected by
re validator.
.build_area_re_formula() synthesises nested random-effect
terms with the lme4 sugar , but the regex in hbm() only
accepted and . The regex now accepts the / separator as
well, so hierarchical- area models built via
area_var = c("province", "regency"), area_re_structure = "nested"
reach brms with the correct formula.
sae_aggregate(method = "weighted") with
all-zero weights produced NaN. The internal normalisation
weights / sum(weights) silently produced NaN when all
weights were zero (and Inf when sum was negative). We now validate that
the weight sum is strictly positive and that all individual weights are
finite, raising informative errors otherwise.
sae_transform() recycled scalar return
values silently. A user passing a reducer function
(e.g. fun = sum, fun = mean) instead of an
element-wise transform (fun = log, fun = exp)
saw silent scalar recycling – every area’s prediction became identical.
We now validate that fun(pred) returns a numeric vector of
the same length as the input and emit an informative error
otherwise.
update_hbm() failed silently when newdata
lacked offset columns. Models fitted with , + (in ), or attach
hidden offset columns () to the model data frame. When the user passed a
that lacked these columns to , brms refused to refit with the unhelpful
error . now detects the case and: Three regression tests added.
Multivariate (mi() / joint) models failed in , , and . brms’s returns a 3-D array of shape (draws x obs x responses) for multivariate formulas (e.g. ); and downstream produced 2-D outputs that broke the construction of . Additionally, requires an explicit argument for multivariate models, which we were not providing. All three functions now detect the multivariate case, default to the first sub-formula’s response (the conventional SAE target), and forward it appropriately. Power users can override via in the argument.
CRITICAL: .add_fixed_pforms() failed on
multivariate formulas. When the user combined joint-imputation
formulas (e.g. ) with / , the helper attempted to append via . brms
refused this because the dpar formula did not specify which response (
or ) the applies to, producing . The helper now detects objects,
extracts the primary response (first sub-formula by convention), and
passes to . Regression tests in cover both the end-to-end flow and the
helper in isolation.
CRITICAL: sampling_variance / silently
corrupted by brms’s default log link on sigma.
When the user supplied , the wrapper stored in and attached it to the
brms formula as . However, brms applies the dpar’s link function before
plugging the linear predictor into the likelihood; for Gaussian /
Lognormal / Student families the default caused the Stan model to
compute instead of . E.g. should give but actually produced – a
catastrophic miscalibration of the Fay-Herriot model. Same bug affected
any user pinning sigma via the generic , and would also have affected
phi for Beta if had not already manually set . The fix forces on the
family object for every pinned dpar, so the offset values are passed
verbatim to the likelihood. Three regression tests added to guard
against this regression.
posterior_interval() and
prior_draws() now re-export the upstream generics
from and respectively, rather than defining new generics with
conflicting signatures. This fixes a name-collision crash that occurred
when was attached together with and the user called on an object: the
error message no longer occurs. The fix follows the standard R package
design pattern used by itself.
sae_benchmark() scale corruption
fix. The previous default weights = rep(1 / n, n)
silently assumed target was a population mean. When users
instead passed a population total, the implied ratio adjustment came out
roughly times too large – benchmarked estimates were scale-corrupt by a
factor of . Fixed by introducing the explicit target_type
argument (see New features) and emitting a message when the default
kicks in.
sae_scale() zero-variance NaN
propagation. When all area predictions were identical,
base::scale() produced NaN throughout,
silently corrupting result_table. Now detected with a
warning; the method returns centred-only (or raw) predictions
instead.
hbm_betalogitnorm() divergent-transitions on
random phi. The prior default
link_phi = "identity" together with the hyperprior let NUTS
propose negative , triggering evaluations. Default is now resolved
conditionally (see New features).
dev-tests library(hbsaems)
auto-attach. The helper file
tests/testthat/dev-tests/helper-dev-setup.R now attaches
the package automatically, fixing 150+ spurious
could not find function errors when dev-tests were run
via testthat::test_dir().
hbm_betalogitnorm() early validation of
spatial_var. When the user supplied
spatial_var referencing a column that did not exist in
data, the error previously surfaced only inside brms with
the unfriendly message “variables can neither be found in ‘data’ nor
in ‘data2’”. The wrapper now validates the column up front and
emits the friendlier “Spatial variable ‘data.”area_var.
hbm() data2 collision with
user-supplied .... Calling hbm() with
both spatial_model = "car" (or "sar") and a
user-supplied data2 = list(...) via ...
crashed with the R-level error “formal argument ‘data2’ matched by
multiple actual arguments”. hbm() builds an internal
data2 = list(M = M) for the spatial weight matrix and also
splices ... into the brms::brm() call,
producing two data2 keys at the site. The fix extracts any
user-supplied from before constructing the brms argument list and merges
it into the internal via – the spatial-matrix slot () supplied by
hbsaems wins on collision, but additional user keys (e.g. auxiliary
matrices for nonlinear models) are preserved. As a related hardening,
attempts to override other internally- managed brms arguments (, , , ,
etc.) via now raise an informative error pointing the user to the
dedicated hbsaems argument.
Test suite reorganisation. Per the recommendation of an external code review, validation-only tests (those exercising only input validation, deprecation warnings, and error paths) have been split out from the heavy integration tests and migrated to . Each block was classified by static analysis: blocks whose only / call appears inside / are CRAN-safe and now run on every check; the remaining ~60 integration tests that actually compile a Stan model stay in and are gated by (or ). Net effect: CRAN test count rose from 391 to ~466 with no measurable runtime increase. Internal helpers and in provide brms-compatible shells for the few tests that need to exercise the deprecation pipeline without compiling Stan.
Project polish. Added
CONTRIBUTING.md documenting the development workflow (test
tiers, mock-stub usage, AST-based formula manipulation patterns,
spelling regeneration); added a light .lintr configuration
for contributors; updated cran-comments.md to describe the
first-submission test environment and intentional notes.
All exported functions documented with full roxygen2
blocks including @param, @return,
@examples, and references where appropriate.
Custom-distribution Stan code stored as separate
.stan files in inst/stan/ (one source of
truth, syntax highlighting, no string-escaping noise).
Test suite reorganised into CRAN-safe unit tests
(tests/testthat/) and heavy integration tests
(tests/testthat/dev-tests/, gated by
skip_on_cran() and excluded via
.Rbuildignore).
hbm(),
hbcc(), hbmc(), hbpc(),
hbsae(), and three example datasets
(data_fhnorm, data_lnln,
data_betalogitnorm, data_binlogitnorm).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.