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  "Title": "Multivariate Regression Association Measure",
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  "Authors@R": "c(person(given = \"Jia-Han\",\nfamily = \"Shih\",\nrole = c(\"aut\",\"cre\"),\nemail = \"jhshih@math.nsysu.edu.tw\"),\nperson(given = \"Yi-Hau\",\nfamily = \"Chen\",\nrole = \"aut\"))",
  "Description": "Implementations of an estimator for the multivariate\nregression association measure (MRAM) proposed in Shih and Chen\n(2026) <doi:10.1016/j.csda.2025.108288> and its associated\nvariable selection algorithm. The MRAM quantifies the\npredictability of a random vector Y from a random vector X\ngiven a random vector Z. It takes the maximum value 1 if and\nonly if Y is almost surely a measurable function of X and Z,\nand the minimum value of 0 if Y is conditionally independent of\nX given Z. The MRAM generalizes the Kendall's tau copula\ncorrelation ratio proposed in Shih and Emura (2021)\n<doi:10.1016/j.jmva.2020.104708> by employing the spatial sign\nfunction. The estimator is based on the nearest neighbor\nmethod, and the associated variable selection algorithm is\nadapted from the feature ordering by conditional independence\n(FOCI) algorithm of Azadkia and Chatterjee (2021)\n<doi:10.1214/21-AOS2073>. For further details, see the paper\nShih and Chen (2026) <doi:10.1016/j.csda.2025.108288>.",
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  "Author": "Jia-Han Shih [aut, cre], Yi-Hau Chen [aut]",
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