Evaluate deterministic M4 parameter and state recovery — multimodal_m4

Recovery aligns latent-state labels before scoring multichannel state effects, and emphasizes posterior-probability calibration, occupancy, and transition recovery rather than treating MAP classification accuracy as the primary criterion. By default the function returns a small five-scenario design and does not launch expensive fitting.

Usage

multimodal_m4_recovery(
  simulation = NULL,
  fit = NULL,
  scenarios = c("clear", "weak", "null", "trait_conditioned", "nuisance_confounded"),
  run = FALSE,
  simulation_args = list(n_person = 60L, n_item = 10L),
  fit_args = list()
)

Arguments

simulation: Optional single M4 simulation.
fit: Optional already-fitted M4 model corresponding to `simulation`.
scenarios: Deterministic development battery used when `run = TRUE`.
run: Whether to execute the small recovery battery. Default `FALSE`.
simulation_args: Named arguments forwarded to simulation.
fit_args: Named arguments forwarded to `fit_multimodal_m4()`.

Value

An `eye_multimodal_m4_recovery`.