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  "Description": "Simulate multivariate correlated data given nonparametric\nmarginals and their joint structure characterized by a Pearson\nor Spearman correlation matrix. The simulator engages the\nproblem from a purely computational perspective. It assumes no\nstatistical models such as copulas or parametric distributions,\nand can approximate the target correlations regardless of\ntheoretical feasibility. The algorithm integrates and advances\nthe Iman-Conover (1982) approach\n<doi:10.1080/03610918208812265> and the Ruscio-Kaczetow\niteration (2008) <doi:10.1080/00273170802285693>. Package\nfunctions are carefully implemented in C++ for squeezing\ncomputing speed, suitable for large input in a manycore\nenvironment. Precision of the approximation and computing speed\nboth substantially outperform various CRAN packages to date.\nBenchmarks are detailed in function examples. A simple\nheuristic algorithm is additionally designed to optimize the\njoint distribution in the post-simulation stage. The heuristic\ndemonstrated good potential of achieving the same level of\nprecision of approximation without the enhanced\nIman-Conover-Ruscio-Kaczetow. The package contains a copy of\nPermuted Congruential Generator.",
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    "SJspearman",
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