## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(gp3bayes)

## -----------------------------------------------------------------------------
bayesian_backend_capabilities()

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

binary_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 = c("participant_covariate", "trial_covariate"),
  interaction = c("condition", "participant_covariate"),
  random_slope = FALSE
)

binary_prepared <- prepare_hierarchical_binary_data(
  binary_sim$data,
  binary_contract,
  condition_levels = c("control", "treatment")
)

binary_spec <- specify_binary_model_with_interaction_prior(
  binary_prepared,
  baseline = 0.35
)

interaction_prior_summary(binary_spec)

## ----eval=FALSE---------------------------------------------------------------
# fit_rstan <- fit_binary_model_backend(
#   binary_spec,
#   backend = "rstan"
# )
# 
# fit_cmdstanr <- fit_binary_model_backend(
#   binary_spec,
#   backend = "cmdstanr"
# )

## ----eval=requireNamespace("detectseparation", quietly=TRUE)------------------
separation_screen <- detect_binary_separation(binary_spec)
separation_screen
plot(separation_screen)

## ----eval=FALSE---------------------------------------------------------------
# loo_a <- compute_psis_loo(fit_a)
# loo_b <- compute_psis_loo(fit_b)
# 
# comparison <- compare_psis_loo(list(contract_a = loo_a, contract_b = loo_b))
# comparison
# 
# weights <- compute_loo_model_weights(comparison, method = "stacking")
# weights

## ----eval=FALSE---------------------------------------------------------------
# sensitivity <- assess_powerscaled_sensitivity(
#   fit_rstan,
#   variable = c("b_Intercept", "b_condition")
# )
# 
# sensitivity
# plot(sensitivity, type = "ecdf")
# plot(sensitivity, type = "quantities")

## ----eval=FALSE---------------------------------------------------------------
# plan <- create_brms_sbc_plan(
#   binary_spec,
#   n_sims = 50,
#   backend = "cmdstanr"
# )
# 
# sbc_result <- run_sbc_plan(plan)
# sbc_result
# plot(sbc_result, type = "rank")
# plot(sbc_result, type = "ecdf")