Simulate advanced dynamic pupil time courses — simulate_advanced_pupil

Generates deterministic hierarchical traces with optional Student-t contamination, heteroskedasticity, ARMA dependence, measurement error, and missingness. The simulator is intended for examples, recovery studies, and failure-path validation; it does not claim physiological realism.

Usage

simulate_advanced_pupil_timecourse(
  n_participants = 24L,
  trials_per_participant = 6L,
  time_points = 41L,
  time_range = c(-500, 2500),
  conditions = c("control", "treatment"),
  family = c("gaussian", "student"),
  residual_scale = 0.08,
  heteroskedastic_strength = 0.35,
  ar = 0.45,
  ma = numeric(),
  participant_sd = 0.12,
  amplitude_condition = 0.22,
  latency_condition = 120,
  outlier_fraction = 0.01,
  missing_fraction = 0.03,
  measurement_error_sd = 0.015,
  student_df = 5,
  seed = 2026
)

Arguments

n_participants: Number of participants.
trials_per_participant: Trials per participant.
time_points: Number of samples per trial.
time_range: Numeric length-two time range in milliseconds.
conditions: Character condition labels.
family: "gaussian" or "student".
residual_scale: Baseline residual SD.
heteroskedastic_strength: Multiplicative time-varying noise strength.
ar: Numeric AR coefficients, length at most 3.
ma: Numeric MA coefficients, length at most 2.
participant_sd: Participant random-intercept SD.
amplitude_condition: Difference in response amplitude for condition 2.
latency_condition: Difference in peak latency for condition 2.
outlier_fraction: Fraction of observations receiving extra contamination.
missing_fraction: Fraction of pupil observations set missing.
measurement_error_sd: Known response-measurement SD; zero disables.
student_df: Degrees of freedom when family = "student".
seed: Random seed.

Value

A gp3bayes_pupil_advanced_simulation object with data and stored truth.