Simulate Joint Distribution

Simulate multivariate correlated data given nonparametric marginal distributions and their covariance structure characterized by a correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover approach (1982) and the Ruscio-Kaczetow iteration (2008) . Package functions are carefully implemented in C++ for pushing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both outperform various CRAN packages to date by a substantial margin. Benchmarks are detailed in function examples. Additionally, a simple heuristic algorithm initially designed to optimize the simulated joint distribution demonstrated not only strong error reduction capability, but also the potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow as a primer, especially when the functional relationships between marginal distributions are highly nonlinear.


Reference manual

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0.2.1 by Charlie Wusuo Liu, a month ago

Browse source code at

Authors: Charlie Wusuo Liu

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, RcppParallel

Suggests R.rsp

Linking to Rcpp, RcppParallel, RcppArmadillo

System requirements: GNU make

See at CRAN