powerbrmsINLA 1.3.0

Bug fixes and validation

• 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.
• Added INLA-independent regression tests covering all three fixes.

Sequential Bayesian analysis (new)

• New 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.
• New 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.
• New 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.
• New 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.

Breaking changes

• In 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).

Bug fixes

• 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.
• Two test files (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.

Other improvements

• All simulation engines now fail early with an informative message when INLA is not installed, instead of failing inside the simulation loop.
• .plot_decision_assurance_curve_from_summary() is no longer exported (it is an internal helper).
• The sequential engine’s default seed is now 123, matching the other engines.
• Fixed the internal .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.
• Removed duplicated internal plotting helpers (single copies retained in utils-helpers.R); pruned stray globalVariables entries; large PNG/PDF artefacts at the package root are excluded from builds via .Rbuildignore.

powerbrmsINLA 1.2.0

Major additions

• New 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.
• New assurance_prior_weights() convenience wrapper for constructing normalised design-prior weights (normal, uniform, beta) over an effect grid.
• New decide_sample_size() function with both assurance mode (design prior) and conditional mode for recommending sample sizes from simulation output.
• New validate_inla_vs_brms() function for spot-checking INLA posterior estimates against brms/Stan.
• Print methods for brms_inla_power, powerbrmsINLA_assurance, and powerbrmsINLA_sample_size objects.

Plotting

• Added plot_assurance_curve() and plot_assurance_with_robustness() for unconditional assurance visualisation.
• Added plot_bf_assurance_curve_smooth(), plot_bf_assurance_curve(), plot_bf_expected_evidence(), and plot_bf_heatmap() for Bayes factor visualisation.
• Added plot_decision_assurance_curve(), plot_decision_threshold_contour(), and add_decision_overlay() for decision-rule visualisation.
• Added plot_design_prior() for visualising design priors.
• Added plot_interaction_surface() for multi-effect grid visualisation.
• Added plot_power_contour(), plot_power_heatmap(), and plot_power_assurance_overlay() for conditional power visualisation.
• Added plot_precision_assurance_curve() and plot_precision_fan_chart().

Engine improvements

• 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.

Bug fixes and housekeeping

• Removed duplicate internal function definitions (.to_inla_family(), .scale_fill_viridis_discrete()).
• Added requireNamespace("MASS") guard for negative binomial data generation.
• Expanded .Rbuildignore to exclude .claude/, .DS_Store, .Rcheck/, and .tar.gz artefacts.
• New test suites for assurance computation, decision helpers, prior bridge, and plotting functions.

powerbrmsINLA 1.1.0

• Added brms_inla_power_parallel() for parallel simulations.
• Added decide_sample_size() and add_decision_overlay() helpers.
• Added new Bayes factor and precision assurance plotting functions.

powerbrmsINLA 1.1.1

• error_sd and group_sd now accept distributional specifications (halfnormal, lognormal, uniform) for variance-uncertainty integration; new validate_sd_spec() helper exported.
• Added validation test suite (test-validation-classical.R, test-validation-bayesassurance.R) and accompanying vignette benchmarking against power.t.test() and bayesassurance::assurance_nd_na().
• CRAN housekeeping: exclude .github, LICENSE.md, and cran-comments.md from the source tarball via .Rbuildignore.