brms_inla_power() now raises a hard error (rather than
a warning) when effect_name does not match a formula-level
fixed-effect term and the automatic data generator is in use. Previously
such a name was silently ignored when building the linear predictor, so
the requested effect was never applied to the simulated data even though
its grid value was recorded. The error message clarifies that the
formula term should be used (e.g.
effect_name = "Condition"), not a fitted coefficient level
(e.g. "Condition1"). When a custom
data_generator is supplied the check is downgraded to a
warning, since the naming convention is then under user control. This
validation now runs before the INLA dependency check.decide_sample_size() in conditional mode now requires
at least one decision target (direction,
threshold, rope_in, or bf10);
supplying none previously produced an arbitrary smallest-n
recommendation. Assurance mode already enforced this.decide_sample_size() no longer mistakes the per-cell
SD-moment summary columns (mean_sampled_error_sd,
sd_sampled_error_sd, mean_sampled_group_sd,
sd_sampled_group_sd) for effect-grid/design columns. Only
sampled_* was excluded previously, which left the
mean_sampled_* / sd_sampled_* columns to
fragment the effect grouping.sequential_design() for prespecifying a sequential
Bayesian analysis: model, analysis priors, monitored effect, decision
metric (direction / threshold / ROPE), per-look success and futility
thresholds, and the planned look schedule. The object carries an MD5
fingerprint of all decision-relevant fields for quotation in
preregistrations; any change to the rules changes the fingerprint.sequential_analysis() for monitoring a real study
as data accumulate: fits the prespecified model with INLA at each
interim look, applies the prespecified stopping rule, and records an
auditable trajectory (estimates, credible intervals, monitored
probabilities, decisions, and any protocol deviations). Refuses to
continue after a recorded stop unless explicitly overridden, in which
case the continuation is logged as a deviation.plot_sequential_monitor() for trajectory plots of
the monitored posterior probability against the prespecified stopping
boundaries, or of the effect estimate with its credible interval across
looks.brms_inla_sequential_trial() simulates the
operating characteristics of a sequential design before data collection:
probabilities of stopping for success / futility / equivalence, expected
sample size, the per-look stopping distribution, and the exaggeration of
effect estimates at early stops. True effects may be fixed scenarios or
drawn from a design prior (sequential assurance). Accepts
sequential_design() objects so the simulated design is
exactly the design that is later monitored.brms_inla_power_sequential() summaries, the column
previously named assurance is now
conditional_power. The old name was misleading: the
quantity is conditional power at a fixed effect-grid value, not
unconditional assurance (which compute_assurance()
provides).brms_inla_power_two_stage() no longer errors when
called with default arguments (error_sd and
obs_per_group previously defaulted to NULL,
which the input validator rejects); defaults are now 1 and 10, matching
brms_inla_power().brms_inla_power_sequential() now returns an object of
class "brms_inla_power", so the print method applies; it
also validates effect_name and target on
entry.sequencial-test.R,
set-up.R) did not match testthat’s file-name conventions
and were silently never executed; they have been renamed
(test-sequential-stopping.R, setup.R),
updated, and now run..plot_decision_assurance_curve_from_summary() is no
longer exported (it is an internal helper)..geom_point_lw() helper, which
passed linewidth to ggplot2::geom_point() on
ggplot2 >= 3.4; the parameter was silently ignored (“Ignoring unknown
parameters” warning) and points rendered at their default size. Point
sizing now uses size, as geom_point expects.utils-helpers.R); pruned stray
globalVariables entries; large PNG/PDF artefacts at the
package root are excluded from builds via
.Rbuildignore.compute_assurance() function for unconditional
Bayesian assurance (O’Hagan & Stevens, 2001) computed as a weighted
average of conditional power over a design prior on the effect
size.assurance_prior_weights() convenience wrapper for
constructing normalised design-prior weights (normal, uniform, beta)
over an effect grid.decide_sample_size() function with both assurance
mode (design prior) and conditional mode for recommending sample sizes
from simulation output.validate_inla_vs_brms() function for spot-checking
INLA posterior estimates against brms/Stan.brms_inla_power,
powerbrmsINLA_assurance, and
powerbrmsINLA_sample_size objects.plot_assurance_curve() and
plot_assurance_with_robustness() for unconditional
assurance visualisation.plot_bf_assurance_curve_smooth(),
plot_bf_assurance_curve(),
plot_bf_expected_evidence(), and
plot_bf_heatmap() for Bayes factor visualisation.plot_decision_assurance_curve(),
plot_decision_threshold_contour(), and
add_decision_overlay() for decision-rule
visualisation.plot_design_prior() for visualising design
priors.plot_interaction_surface() for multi-effect grid
visualisation.plot_power_contour(),
plot_power_heatmap(), and
plot_power_assurance_overlay() for conditional power
visualisation.plot_precision_assurance_curve() and
plot_precision_fan_chart().brms_inla_power() now supports multi-effect grids
(data.frame effect_grid), brms-to-INLA prior translation
with full audit trail, marginal-likelihood Bayes factors
(bf_method = "marglik"), and automatic INLA thread
detection.brms_inla_power_sequential() rewritten with
multi-effect support and prior translation.brms_inla_power_two_stage() now uses the modernised
engine internally..to_inla_family(),
.scale_fill_viridis_discrete()).requireNamespace("MASS") guard for negative
binomial data generation..Rbuildignore to exclude
.claude/, .DS_Store, .Rcheck/,
and .tar.gz artefacts.brms_inla_power_parallel() for parallel
simulations.decide_sample_size() and
add_decision_overlay() helpers.error_sd and group_sd now accept
distributional specifications (halfnormal,
lognormal, uniform) for variance-uncertainty
integration; new validate_sd_spec() helper exported.test-validation-classical.R,
test-validation-bayesassurance.R) and accompanying vignette
benchmarking against power.t.test() and
bayesassurance::assurance_nd_na()..github,
LICENSE.md, and cran-comments.md from the
source tarball via .Rbuildignore.
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.