flexIC is a high-precision Iman–Conover engine for generating continuous variables that preserve rank correlation with marginal fidelity. It offers tunable convergence control, allowing you to aggressively reduce rank-correlation distortion—at the cost of a few extra milliseconds.
Use it to:
- Simulate data with a target Spearman or Kendall structure
- Preserve original variable distributions via back-ranking
- Validate or stress-test statistical methods under structured dependence
Most Iman–Conover implementations:
- Run once with no convergence check
- Do not guarantee low error
- Break marginal shapes in edge cases
flexIC:
- Iterates until max abs rank-correlation error ≤ ε
- Keeps original marginal shapes intact
- Returns detailed error diagnostics
- Finishes in milliseconds on typical datasets
# Development version (until on CRAN)
remotes::install_github("TheotherDrWells/flexIC")