flexIC is a high-precision ImanâConover engine for generating continuous variables that preserve rank correlation with marginal fidelity. It offers tunable convergence control, allowing you to aggressively reduce rank-correlation distortionâat 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
ð Why useflexIC
?
Most ImanâConover 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
ð¦ Installation```r # Development version (until on CRAN) remotes::install_github(âTheotherDrWells/flexICâ)
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