{ "_id": "6a2a82e37b7a29ca60048334", "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. The package contains a copy of\nPermuted Congruential Generator.", "License": "GPL-3", "Encoding": "UTF-8", "SystemRequirements": "GNU make", "VignetteBuilder": "R.rsp", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-11 09:38:05 UTC", "User": "root" }, "Config/pak/sysreqs": "make", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2024-01-15 02:47:15 UTC", "RemoteUrl": "https://github.com/cran/SimJoint", "RemoteRef": "HEAD", "RemoteSha": "625cc4653cddb46546dad3ddc725fc211cd1118d", "MD5sum": "5ba5d3dede63bba627422130452c5506", "_user": "cran", "_type": "src", "_file": "SimJoint_0.3.12.tar.gz", "_fileid": "6bb4ce36f54b471eaaf8141179602c2cef467115f02ff9376e04c19fb87798be", "_filesize": 436032, "_sha256": "6bb4ce36f54b471eaaf8141179602c2cef467115f02ff9376e04c19fb87798be", "_created": "2026-06-11T09:38:05.000Z", "_published": "2026-06-11T09:41:55.950Z", "_distro": "noble", "_jobs": [ { "job": 80766812187, "time": 180, "config": "linux-devel-arm64", "r": "4.7.0", 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probability mass function", "topics": [ "LHSpmf" ] }, { "page": "postSimOpt", "title": "Post simulation optimization", "topics": [ "postSimOpt" ] }, { "page": "SJpearson", "title": "Simulate joint given marginals and Pearson correlations.", "topics": [ "SJpearson" ] }, { "page": "SJpearsonPMF", "title": "Simulate joint with marginal PMFs and Pearson correlations.", "topics": [ "SJpearsonPMF" ] }, { "page": "SJspearman", "title": "Simulate joint given marginals and Spearman correlations.", "topics": [ "SJspearman" ] }, { "page": "SJspearmanPMF", "title": "Simulate joint with marginal PMFs and Spearman correlations.", "topics": [ "SJspearmanPMF" ] }, { "page": "xSJpearson", "title": "Simulate joint given marginals, Pearson correlations and uncorrelated support matrix.", "topics": [ "xSJpearson" ] }, { "page": "xSJpearsonPMF", "title": "Simulate joint with marginal PMFs, Pearson correlations and uncorrelated support matrix.", "topics": [ "xSJpearsonPMF" ] } ], "_rundeps": [ "Rcpp", 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