Evaluate dynamic-state recovery under misclassification — dynamic_irtree

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

dynamic_irtree_recovery(grid = expand.grid(state_misclassification = c(0, 0.05, 0.15),
  missing_state = c(0, 0.10), stringsAsFactors = FALSE), replications = 20L,
  spec = dynamic_irtree_spec(engine = "multinomial"), base_seed = 1L)

Arguments

grid: Scenario grid.
replications: Replications per scenario.
spec: Dynamic IRTree specification.
base_seed: Base seed.

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.