A Reproducible 0.2.0 Release Case Study

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Purpose

This case study exercises the stable 0.2.0 workflow on deterministic synthetic data. The vignette evaluates every backend-independent stage and leaves the optional Stan fits unevaluated so package documentation remains portable.

1. Simulate known data

simulation <- simulate_hierarchical_binary_data(
  n_participants = 24,
  trials_per_participant = 12,
  n_items = 8,
  random_slope_sd = 0,
  seed = 202602
)

2. Declare the analysis contract

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"
)

readiness <- audit_model_readiness(simulation$data, contract)
readiness
#> <gp3bayes_readiness_audit>
#>   Family: binary
#>   Rows: 288
#>   Status: ready
#>   Ready: TRUE
#>   Checks: 22 passed, 0 warnings, 0 failures

3. Preflight the design

design <- audit_design_support(
  simulation$data,
  contract,
  separation = FALSE,
  strict_readiness = TRUE
)
design
#> <gp3bayes_design_support_audit>
#>   Status: review
#>               component       status
#>      standard_readiness         pass
#>        strict_readiness         pass
#>             missingness         pass
#>     fixed_effect_design       review
#>  random_effects_support         pass
#>              separation not_assessed
#>   Automatic model changes: FALSE

4. Prepare and specify

prepared <- prepare_hierarchical_binary_data(
  simulation$data,
  contract,
  condition_levels = c("control", "treatment"),
  scale_predictors = "trial_covariate"
)

specification <- specify_binary_model(
  prepared,
  baseline = 0.35
)

prior_check <- check_binary_prior_predictive(
  specification,
  draws = 200,
  seed = 202603
)
prior_check
#> <gp3bayes_binary_prior_predictive_check>
#>   Adequate: TRUE
#>   Draws: 200
#>   Failed checks: 0
#>   Backend: none
#>   Fit performed: FALSE

5. Freeze analysis provenance

sensitivity_plan <- create_sensitivity_suite_plan(
  prior_scale = TRUE,
  psis_loo = TRUE
)

manifest <- create_analysis_manifest(
  specification = specification,
  estimands = "standardized_probability_contrast",
  sensitivity_plan = sensitivity_plan,
  seed = 202604,
  label = "gp3bayes 0.2.0 synthetic release case"
)

frozen_manifest <- freeze_analysis_manifest(manifest)
frozen_manifest
#> <gp3bayes_analysis_manifest>
#>   Version: 0.2
#>   Label: gp3bayes 0.2.0 synthetic release case
#>   Family: binary
#>   Data: 288 x 8
#>   Data hash: f3612a97dabe7adffe8782487b233903
#>   Frozen: TRUE
#>   Manifest hash: 9acf100d29ba0b0c41fe1ef42b232ac2

6. Optional dual-backend fitting

fit_rstan <- fit_binary_model_backend(
  specification,
  backend = "rstan",
  chains = 2,
  iter = 2000,
  warmup = 1000,
  cores = 2,
  seed = 202604
)

fit_cmdstanr <- fit_binary_model_backend(
  specification,
  backend = "cmdstanr",
  chains = 2,
  iter = 2000,
  warmup = 1000,
  cores = 2,
  seed = 202604
)

7. Unified posterior review

diagnostics <- diagnose_model_fit(fit_cmdstanr)
posterior <- summarise_model_posterior(fit_cmdstanr)
ppc <- check_model_ppc(fit_cmdstanr, draws = 500, seed = 202605)
estimands <- estimate_model_estimands(fit_cmdstanr)
loo_result <- compute_psis_loo(fit_cmdstanr)
suite <- run_sensitivity_suite(fit_cmdstanr, sensitivity_plan)

8. Cross-backend consistency

parity <- audit_backend_parity(fit_rstan, fit_cmdstanr)
parity
plot(parity)

9. Evidence and compatibility

evidence <- collect_model_evidence(
  fit = fit_cmdstanr,
  design = design,
  diagnostics = diagnostics,
  posterior = posterior,
  ppc = ppc,
  estimands = estimands,
  loo = loo_result,
  sensitivity = suite,
  manifest = frozen_manifest
)

fit_schema <- freeze_gp3bayes_schema(
  capture_gp3bayes_schema(fit_cmdstanr)
)

evidence
model_workflow_status(evidence)

The end product is an inspectable chain from design contract to evidence inventory. At no stage does the package infer emotion, cognition, diagnosis, causality, model adequacy, robustness, or a preferred model automatically.

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.
Health stats visible at Monitor.