Specify an advanced governed pupil time-course model — specify_advanced_pupil_timecourse

Builds the 0.5 advanced model contract without compiling or fitting Stan. The function is additive to the frozen 0.4 API: it consumes the same prepared pupil data but returns a distinct advanced specification.

Usage

specify_advanced_pupil_timecourse_model(
  prepared,
  temporal_structure = c("smooth", "linear", "gaussian_process"),
  family = c("gaussian", "student"),
  residual_scale = c("constant", "condition", "time", "condition_time"),
  distribution = NULL,
  smooth_basis_dimension = 10L,
  gp_spec = create_pupil_gp_spec(),
  condition_trajectory = NULL,
  autocorrelation = c("none", "ar1", "ar2", "arma11"),
  participant_trajectory = c("none", "factor_smooth"),
  item_effects = NULL,
  covariates = character(),
  measurement_model = NULL,
  missingness_model = NULL,
  prior_scales = NULL,
  predictive_target = c("new_trial_known_participant", "new_participant",
    "future_segment", "new_sample_known_trial"),
  allow_high_complexity = FALSE
)

Arguments

prepared: A prepared 0.4 pupil object or compatible data frame.
temporal_structure: "smooth", "linear", or "gaussian_process".
family: "gaussian" or robust "student".
residual_scale: Residual-scale model: constant, condition, time, or condition-by-time.
distribution: Optional object from specify_pupil_distribution(). When supplied, its family and residual-scale declarations override the corresponding scalar arguments.
smooth_basis_dimension: Basis dimension for smooth mean trajectories.
gp_spec: A GP configuration from create_pupil_gp_spec().
condition_trajectory: Whether condition-specific trajectories are included. Defaults to TRUE when a condition column exists.
autocorrelation: One of "none", "ar1", "ar2", "arma11", or a bounded object from create_pupil_arma_spec().
participant_trajectory: "none" or "factor_smooth".
item_effects: Include a random item intercept when an item column exists.
covariates: Additional declared covariates.
measurement_model: Optional known-uncertainty declaration.
missingness_model: Optional MAR-oriented missingness declaration.
prior_scales: Optional named numeric prior-scale overrides.
predictive_target: Declared prediction target inherited from the 0.4 validation vocabulary.
allow_high_complexity: Permit specifications flagged by the complexity audit. This is an explicit opt-in, not automatic model approval.

Value

A gp3bayes_pupil_advanced_specification object.