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Governed Predictive Model Comparison

library(gp3bayes)
sim <- simulate_advanced_pupil_timecourse(
  n_participants = 12,
  trials_per_participant = 4,
  time_points = 31,
  seed = 3050
)

base <- specify_advanced_pupil_timecourse_model(sim$data, temporal_structure = "smooth", family = "gaussian")
suite <- create_pupil_advanced_sensitivity_suite(base)
suite
#> <gp3bayes_pupil_advanced_sensitivity_suite>
#>   Scenarios: 10 
#>                   scenario          dimension            value
#>                   baseline           baseline         declared
#>         likelihood_student             family          student
#>            sigma_condition     residual_scale        condition
#>                 sigma_time     residual_scale             time
#>       sigma_condition_time     residual_scale   condition_time
#>                     ac_ar1    autocorrelation              ar1
#>                     ac_ar2    autocorrelation              ar2
#>                  ac_arma11    autocorrelation           arma11
#>            temporal_linear temporal_structure           linear
#>  temporal_gaussian_process temporal_structure gaussian_process

The sensitivity suite is a pre-fit registry of alternatives. It does not fit or rank models.

robust <- materialize_pupil_advanced_sensitivity_scenario(suite, "likelihood_student")
gp <- materialize_pupil_advanced_sensitivity_scenario(suite, "temporal_gaussian_process")

fit_smooth <- fit_advanced_pupil_model_backend(base, backend = "cmdstanr")
fit_robust <- fit_advanced_pupil_model_backend(robust, backend = "cmdstanr")
fit_gp <- fit_advanced_pupil_model_backend(gp, backend = "cmdstanr")

models <- create_pupil_model_set(
  smooth_gaussian = fit_smooth,
  smooth_student = fit_robust,
  gp_gaussian = fit_gp,
  predictive_target = "future_segment"
)

cmp <- compare_pupil_models(models, criterion = "loo")
pupil_model_comparison_table(cmp)
plot_pupil_model_comparison(cmp)
pupil_model_weights(cmp, method = "stacking")

Weights are returned only as explicit evidence. gp3bayes does not automatically average predictions or declare the highest-weight model substantively correct.

Leave-future-out is an explicit refit workflow

lfo_plan <- create_pupil_lfo_plan(
  fit_smooth,
  initial_fraction = 0.60,
  horizon = 5,
  step = 5,
  max_refits = 6
)
lfo_plan

# No refit occurs unless execute = TRUE.
lfo_result <- validate_pupil_leave_future_out(
  fit_smooth,
  lfo_plan,
  execute = TRUE,
  cores = 1
)
plot_pupil_lfo(lfo_result)

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.