{ "_id": "6a250c7c4b233be198388077", "Package": "KPC", "Type": "Package", "Title": "Kernel Partial Correlation Coefficient", "Version": "0.1.3", "Authors@R": "c(person(\"Zhen\", \"Huang\", email = \"zh2395@columbia.edu\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Nabarun\", \"Deb\", email = \"nd2560@columbia.edu\",\nrole = \"ctb\"),\nperson(\"Bodhisattva\", \"Sen\", email = \"bodhi@stat.columbia.edu\",\nrole = \"ctb\"),\nperson(\"Benjamin\", \"Lang\", email = \"benjamin_lang@icloud.com\",\nrole = \"ctb\"))", "Maintainer": "Zhen Huang ", "Description": "Implementations of two empirical versions the kernel\npartial correlation (KPC) coefficient and the associated\nvariable selection algorithms. KPC is a measure of the strength\nof conditional association between Y and Z given X, with X, Y,\nZ being random variables taking values in general topological\nspaces. As the name suggests, KPC is defined in terms of\nkernels on reproducing kernel Hilbert spaces (RKHSs). The\npopulation KPC is a deterministic number between 0 and 1; it is\n0 if and only if Y is conditionally independent of Z given X,\nand it is 1 if and only if Y is a measurable function of Z and\nX. One empirical KPC estimator is based on geometric graphs,\nsuch as K-nearest neighbor graphs and minimum spanning trees,\nand is consistent under very weak conditions. The other\nempirical estimator, defined using conditional mean embeddings\n(CMEs) as used in the RKHS literature, is also consistent under\nsuitable conditions. Using KPC, a stepwise forward variable\nselection algorithm KFOCI (using the graph based estimator of\nKPC) is provided, as well as a similar stepwise forward\nselection algorithm based on the RKHS based estimator. For more\ndetails on KPC, its empirical estimators and its application on\nvariable selection, see Huang, Z., N. Deb, and B. Sen (2022).\n“Kernel partial correlation coefficient – a measure of\nconditional dependence” (URL listed below). When X is empty,\nKPC measures the unconditional dependence between Y and Z,\nwhich has been described in Deb, N., P. Ghosal, and B. Sen\n(2020), “Measuring association on topological spaces using\nkernels and geometric graphs” ,\nand it is implemented in the functions KMAc() and Klin() in\nthis package. The latter can be computed in near linear time.", "License": "GPL-3", "Encoding": "UTF-8", "URL": "https://www.jmlr.org/papers/v23/21-493.html,\nhttps://arxiv.org/abs/2012.14804, https://github.com/zh2395/KPC", "LazyData": "true", "RoxygenNote": "7.3.3", "Repository": "https://zh2395.r-universe.dev", "Date/Publication": "2026-05-08 18:38:55 UTC", "RemoteUrl": "https://github.com/zh2395/kpc", "RemoteRef": "HEAD", "RemoteSha": "569a3c3c062ab07de92045247852e5a89f76dab0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-07 06:12:01 UTC", "User": "root" }, "Author": "Zhen Huang [aut, cre],\nNabarun Deb [ctb],\nBodhisattva Sen [ctb],\nBenjamin Lang [ctb]", "MD5sum": "fe11558ff65d6bdcd2c2376a40c15653", "_user": "zh2395", "_type": "src", "_file": "KPC_0.1.3.tar.gz", "_fileid": "f326e4bf21d484d78242b131fc0c5e79819dd0dfc3d576d80a638312e7b6f2aa", "_filesize": 239019, "_sha256": "f326e4bf21d484d78242b131fc0c5e79819dd0dfc3d576d80a638312e7b6f2aa", "_created": "2026-06-07T06:12:01.000Z", 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