The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Computational Governance and Model Cards

library(gp3bayes)
sim <- simulate_advanced_pupil_timecourse(
  n_participants = 10,
  trials_per_participant = 4,
  time_points = 35,
  seed = 3070
)

spec <- specify_advanced_pupil_timecourse_model(
  sim$data,
  temporal_structure = "gaussian_process",
  gp_spec = create_pupil_gp_spec("matern32", "approximate", k = 30),
  residual_scale = "condition_time",
  participant_trajectory = "none",
  predictive_target = "new_trial_known_participant"
)

Complexity is audited before Stan

budget <- audit_pupil_computational_budget(spec)
budget
#> <gp3bayes_pupil_complexity_audit>
#>   Status: ok 
#>   Rows: 1400 
#>   Participants: 10 
#>   Series: 40 
#>               check status
#>                rows     ok
#>              series     ok
#>      approximate_gp     ok
#>  layered_complexity     ok
#>                                                message
#>                                     1400 analysis rows
#>                            40 participant/trial series
#>                             approximate GP with k = 30
#>  2 advanced complexity layers requested simultaneously
plot_pupil_model_complexity(budget)

The complexity gate is not a statistical adequacy test. It is a reproducible guard against accidentally requesting models that combine many expensive layers or exact Gaussian processes over very large grids.

Model card

card <- pupil_model_card(spec)
card
#> <gp3bayes_pupil_model_card>
#>                   field                       value
#>        gp3bayes_version                  0.5.0.9000
#>           fit_performed                       FALSE
#>                 backend                        none
#>                    rows                        1400
#>            participants                          10
#>              conditions                           2
#>                  family                    gaussian
#>      temporal_structure            gaussian_process
#>          residual_scale              condition_time
#>         autocorrelation                        none
#>  participant_trajectory                        none
#>       measurement_model                       FALSE
#>       missingness_model                       FALSE
#>       predictive_target new_trial_known_participant
#>       complexity_status                          ok
#> Governance:
#>   - No automatic preprocessing, interpolation, exclusion, or model selection.
#>   - No automatic cognitive-state, causal, or adequacy interpretation.
#>   - Measurement and missingness models remain assumption-conditional.
#>   - Predictive comparison is tied to an explicitly declared target.
pupil_model_card_table(card)
#>                     field                       value
#> 1        gp3bayes_version                  0.5.0.9000
#> 2           fit_performed                       FALSE
#> 3                 backend                        none
#> 4                    rows                        1400
#> 5            participants                          10
#> 6              conditions                           2
#> 7                  family                    gaussian
#> 8      temporal_structure            gaussian_process
#> 9          residual_scale              condition_time
#> 10        autocorrelation                        none
#> 11 participant_trajectory                        none
#> 12      measurement_model                       FALSE
#> 13      missingness_model                       FALSE
#> 14      predictive_target new_trial_known_participant
#> 15      complexity_status                          ok

A model card records family, temporal structure, residual scale, autocorrelation, data dimensions, measurement/missingness declarations, predictive target, complexity status, and governance text. It is designed to support methods supplements and audit trails without becoming a validity certificate.

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.