{ "_id": "6ad5c792d423e4b0c576c580", "Package": "SimJoint", "Type": "Package", "Title": "Simulate Joint Distribution", "Version": "0.3.12", "Author": "Charlie Wusuo Liu", "Maintainer": "Charlie Wusuo Liu ", "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 and the Ruscio-Kaczetow\niteration (2008) . 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. 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