gp3bayes

gp3bayes is an independent R package for transparent, contract-first Bayesian workflows for repeated-measures and hierarchical behavioural data.

Scope

The current package version is gp3bayes 0.2.0.

The package provides:

• explicit model contracts and standard or strict model-readiness audits;
• deterministic hierarchical binary and positive-duration simulation with stored truth;
• recorded outcome, condition, unit, missing-value, coding, and scaling decisions;
• reusable transformation recipes with replay, inversion, and validation;
• inspectable backend-independent prior specifications and prior predictive checks;
• restricted Bernoulli-logit and lognormal model construction;
• optional full-MCMC fitting through brms with either rstan or cmdstanr;
• R-hat, bulk/tail ESS, divergence, treedepth, and energy diagnostics;
• family-specific posterior summaries and detailed posterior predictive checks;
• design-standardised binary probability contrasts and duration median, ratio, and predictive-quantile estimands;
• prior-scale, power-scaling, structural, deletion, contrast-coding, predictor-scaling, and duration-unit sensitivity workflows;
• PSIS-LOO diagnostics, influence assessment, model comparison, model weights, and optional exact K-fold validation;
• fixed-effects separation screening and simulation-based calibration;
• specification traceability and conservative structured reporting without automatic adequacy, robustness, exclusion, or model-selection claims.

The approved model-family scope remains restricted to:

1. hierarchical Bernoulli-logit models for binary trial-level outcomes;
2. hierarchical lognormal models for strictly positive uncensored durations.

Core contract, validation, simulation, preparation, transformation, specification, and prior-predictive functionality does not require Gazepoint hardware, Gazepoint exports, gp3tools, proprietary software, private data, or a Bayesian backend. Full-MCMC fitting requires brms and one supported sampling backend: rstan or cmdstanr.

Development 0.4.0.9000: Bayesian dynamic pupillometry

The repository development line 0.4.0.9000 introduces a governed Bayesian dynamic-pupillometry model family. The archived stable repository release remains 0.2.0 (v0.2.0); the CRAN badge above reports the CRAN release separately. The 0.4.0.9000 functionality described here is development functionality and is not represented as a CRAN release.

The pupil workflow consumes an explicitly mapped and audited time series rather than silently performing generic pupil preprocessing. Blink/interpolation status, baseline decisions, gaze/PFE information, luminance, contrast, units, device metadata, and upstream provenance can be declared and audited. The first approved model family is a restricted Gaussian hierarchical time-course model with package-constructed temporal structure, grouping effects, scale-aware priors, and optional declared temporal dependence.

pupil_simulation <- simulate_pupil_timecourse(
  n_participants = 4,
  trials_per_participant = 2,
  n_items = 4,
  sampling_frequency = 20,
  time_window = c(-0.2, 1.0),
  baseline_window = c(-0.2, 0),
  seed = 2026
)

pupil_contract <- create_pupil_contract(
  outcome_col = "pupil_mm",
  participant_col = "participant_id",
  trial_col = "trial_id",
  item_col = "item_id",
  condition_col = "condition",
  time_col = "event_time",
  pupil_unit = "millimetres",
  sampling_frequency = 20,
  time_unit = "seconds",
  eye = "combined",
  validity_col = "valid",
  interpolation_col = "interpolated",
  blink_col = "blink",
  gaze_x_col = "gaze_x",
  gaze_y_col = "gaze_y",
  luminance_col = "luminance",
  baseline_window = c(-0.2, 0),
  baseline_method = "none",
  preprocessing_provenance = "gp3bayes deterministic simulator"
)

pupil_prepared <- prepare_pupil_timecourse(
  pupil_simulation$data,
  pupil_contract,
  baseline_operation = "subtract",
  baseline_window = c(-0.2, 0)
)

pupil_readiness <- audit_pupil_readiness(pupil_prepared)
pupil_specification <- specify_pupil_timecourse_model(
  pupil_prepared,
  smooth_basis_dimension = 5,
  autocorrelation = "none"
)

Model fitting is deliberately separate from preprocessing and does not itself establish convergence, model adequacy, causal identification, or a psychological interpretation of pupil change.

0.2.0 stabilization layer

gp3bayes 0.2.0 adds a stable family-neutral workflow API, analysis manifests and reproducibility reports, pre-fit design-support diagnostics, unified sensitivity/evidence inventories, backend-environment and posterior-summary parity audits, and serialized-object schema checks. These layers compose the existing binary and duration workflows without adding unrestricted formulas, automatic model selection, automatic exclusion, or automatic adequacy/causal claims.

design <- audit_design_support(data, contract)
manifest <- create_analysis_manifest(specification, seed = 2026)
manifest <- freeze_analysis_manifest(manifest)
capabilities <- backend_capabilities()

Model contracts

create_model_contract() records the approved methodological specification and neutral column mappings for one initial model family. Creating a contract does not validate data, fit a model, or establish model adequacy.

binary_contract <- create_model_contract(
  family = "binary",
  outcome_col = "selected",
  participant_col = "participant_id",
  item_col = "stimulus_id",
  trial_col = "trial_id",
  condition_col = "condition"
)

binary_contract

## <gp3bayes_model_contract>
##   Family: binary
##   Likelihood: Bernoulli
##   Link: logit
##   Outcome: selected
##   Participant: participant_id
##   Item: stimulus_id
##   Condition: condition
##   Random slope requested: FALSE
##   Fitting performed: FALSE

Readiness audits

audit_model_readiness() evaluates observable data requirements before formula construction or model fitting. Failures block progression, whereas warnings identify structures requiring review.

binary_data <- data.frame(
  participant_id = rep(c("p1", "p2"), each = 4),
  stimulus_id = rep(paste0("s", 1:4), times = 2),
  trial_id = rep(1:4, times = 2),
  condition = rep(c("control", "treatment"), times = 4),
  selected = c(0, 1, 0, 1, 1, 0, 1, 0)
)

readiness_audit <- audit_model_readiness(
  binary_data,
  binary_contract
)

readiness_audit

## <gp3bayes_readiness_audit>
##   Family: binary
##   Rows: 8
##   Status: ready
##   Ready: TRUE
##   Checks: 18 passed, 0 warnings, 0 failures

Model specifications

build_model_formula() translates the approved contract into an R formula, while create_prior_specification() records family-appropriate priors without creating backend-specific objects. A ready audit, formula, contract, and validated priors can then be combined into one inspectable model specification.

binary_priors <- create_prior_specification(
  binary_contract,
  baseline = 0.5
)

binary_specification <- create_model_specification(
  binary_contract,
  readiness_audit,
  binary_priors
)

binary_specification

## <gp3bayes_model_specification>
##   Family: binary
##   Formula: selected ~ condition + (1 | participant_id) + (1 | stimulus_id)
##   Readiness status: ready
##   Readiness warnings: 0
##   Prior classes: Intercept, b, sd
##   Backend: none
##   Fit performed: FALSE

Hierarchical binary workflow foundation

The backend-independent binary workflow can simulate known hierarchical data-generating processes, prepare neutral long-format data, construct a restricted model specification, and evaluate prior predictive plausibility. No model is fitted and no posterior draws are produced.

binary_simulation <- simulate_hierarchical_binary_data(
  n_participants = 12,
  trials_per_participant = 8,
  n_items = 6,
  random_slope_sd = 0,
  seed = 2026
)

binary_workflow_contract <- create_model_contract(
  family = "binary",
  outcome_col = "selected",
  participant_col = "participant_id",
  item_col = "item_id",
  trial_col = "trial_id",
  condition_col = "condition",
  predictors = "trial_covariate"
)

binary_prepared <- prepare_hierarchical_binary_data(
  binary_simulation$data,
  binary_workflow_contract,
  condition_levels = c("control", "treatment"),
  scale_predictors = "trial_covariate"
)

binary_workflow_specification <- specify_binary_model(
  binary_prepared,
  baseline = 0.35
)

binary_prior_check <- check_binary_prior_predictive(
  binary_workflow_specification,
  draws = 100,
  seed = 2027
)

binary_prior_check

## <gp3bayes_binary_prior_predictive_check>
##   Adequate: TRUE
##   Draws: 100
##   Failed checks: 0
##   Backend: none
##   Fit performed: FALSE

Restricted binary model fitting

translate_binary_model_to_brms() converts an approved package specification into a restricted Bernoulli-logit brms representation without compiling or fitting a model.

fit_binary_model() retains the original fixed rstan fitting route. For backend-portable full-MCMC fitting, fit_binary_model_backend() accepts either backend = "rstan" or backend = "cmdstanr" while preserving the same approved family, formula, priors, and sampling contract.

Neither interface accepts unrestricted formulas, arbitrary model families, alternative inference algorithms, user-supplied Stan programs, or arbitrary backend arguments.

if (requireNamespace("brms", quietly = TRUE)) {
  backend_specification <- translate_binary_model_to_brms(
    binary_workflow_specification
  )

  backend_specification
}

A returned fit does not by itself establish convergence, posterior adequacy, causal identification, or substantive validity. Those assessments require separate diagnostic and reporting gates.

Binary posterior validation

Approved binary fits can be assessed with conservative numerical sampling diagnostics, posterior summaries, posterior predictive checks, prior-scale sensitivity, simulation-based recovery, and structured Markdown reports. A threshold pass is not an automatic convergence or posterior-adequacy claim.

diagnostics <- diagnose_binary_fit(binary_fit)
posterior <- summarise_binary_posterior(binary_fit)
predictive <- check_binary_posterior_predictive(binary_fit)

Hierarchical lognormal duration workflow

The duration workflow supports strictly positive, finite, uncensored durations with an explicit recorded unit. It provides deterministic simulation, preparation, inspectable priors, prior predictive checks, and restricted optional full-MCMC fitting through brms with either rstan or cmdstanr.

duration_simulation <- simulate_hierarchical_duration_data(seed = 2026)
duration_contract <- create_model_contract(
  family = "duration",
  outcome_col = "duration",
  participant_col = "participant_id",
  item_col = "item_id",
  trial_col = "trial_id",
  condition_col = "condition",
  outcome_unit = "milliseconds"
)
duration_prepared <- prepare_hierarchical_duration_data(
  duration_simulation$data,
  duration_contract,
  condition_levels = c("control", "treatment")
)
duration_specification <- specify_duration_model(
  duration_prepared,
  baseline = 500
)

Duration posterior validation

Approved lognormal duration fits support the same conservative diagnostic contract as binary fits, together with positive-scale posterior predictive checks, prior sensitivity, simulation-based recovery, and structured reports. Exponentiated population coefficients are conditional median ratios, not automatically causal effects.

duration_diagnostics <- diagnose_duration_fit(duration_fit)
duration_posterior <- summarise_duration_posterior(duration_fit)
duration_predictive <- check_duration_posterior_predictive(duration_fit)

Citation

Citation metadata are provided in both CITATION.cff and inst/CITATION. After installing the package, obtain the R-formatted citation for the installed version with:

citation("gp3bayes")

The repository currently represents release version 0.2.0.

For archived software citation:

• Current CRAN release: gp3bayes 0.1.1 — CRAN
• Concept DOI for all releases — 10.5281/zenodo.21518698
• Latest archived Zenodo version currently documented in the repository: gp3bayes 0.2.0 — 10.5281/zenodo.21880165

Release status

gp3bayes 0.2.0 is the current package version.

The 0.2.0 API provides contract-first Bernoulli-logit and lognormal-duration workflows; backend-portable full-MCMC fitting through brms with either rstan or cmdstanr; advanced sensitivity, PSIS-LOO, model-weighting, separation-screening, and simulation-based calibration workflows; strict specification closure; transformation replay; explicit posterior estimands; detailed posterior predictive checks; and optional exact K-fold validation.

The repository release version is gp3bayes 0.2.0; CRAN availability is reported by the status badge above.

Interpretation boundaries

Behavioural, gaze, pupil, and physiological measurements do not directly reveal emotion, stress, cognition, comprehension, personality, diagnosis, deception, intention, or other latent psychological states.

Associations must not be described as causal effects unless the study design and target estimand justify causal interpretation.

Advanced optional workflows

gp3bayes 0.2.0 includes conservative optional adapters for power-scaled sensitivity through priorsense, PSIS-LOO diagnostics and model weights through loo, fixed-effects separation screening through detectseparation, simulation-based calibration through SBC, and full-MCMC backend selection between rstan and cmdstanr.

Dedicated binary and duration pathology generators make documented failure cases directly reproducible.

These extensions do not introduce unrestricted formulas, arbitrary model families, automatic model selection, automatic exclusion, or automatic adequacy claims.

Licence

gp3bayes is released under the MIT License.

Specification closure

The 0.2.0 API closes the remaining contract-level requirements with strict readiness audits, exact transformation replay, first-class probability/median/tail estimands, governed structural and deletion sensitivity, duration-unit invariance, detailed posterior predictive checks, and optional exact K-fold validation.

These functions remain inside the two approved Bernoulli-logit and positive uncensored lognormal-duration contracts. They do not add arbitrary formulas, likelihoods, automatic model selection, automatic exclusions, or causal claims.

audit_model_readiness_strict(data, contract)
create_transformation_recipe(prepared)
estimate_standardized_probability_contrast(fit)
estimate_standardized_duration_estimands(fit)
check_binary_ppc_details(fit)
check_duration_ppc_details(fit)
gp3bayes_specification_traceability()