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
flexIC?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
```r # Development version (until on CRAN) remotes::install_github(“TheotherDrWells/flexIC”)