{ "_id": "6aca8ce0fe5cc25786e8b4c3", "Package": "rMVP", "Type": "Package", "Title": "Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS\nTool", "Version": "1.4.6", "Date": "2026-03-10", "Authors@R": "c( person(\"Lilin\", \"Yin\", role = \"aut\"), \nperson(\"Haohao\", \"Zhang\", role = \"aut\"),\nperson(\"Zhenshuang\", \"Tang\", role = \"aut\"),\nperson(\"Jingya\", \"Xu\", role = \"aut\"),\nperson(\"Dong\", \"Yin\", role = \"aut\"),\nperson(\"Zhiwu\", \"Zhang\", role = \"aut\"),\nperson(\"Xiaohui\", \"Yuan\", role = \"aut\"),\nperson(\"Mengjin\", \"Zhu\", role = \"aut\"),\nperson(\"Shuhong\", \"Zhao\", role = \"aut\"),\nperson(\"Xinyun\", \"Li\", role = \"aut\"),\nperson(\"Qishan\", \"Wang\", role = \"ctb\"),\nperson(\"Feng\", \"Tian\", role = \"ctb\"),\nperson(\"Hyunmin\", \"Kang\", role = \"ctb\"),\nperson(\"Xiang\", \"Zhou\", role = \"ctb\"),\nperson(\"Xiaolei\", \"Liu\", role = c(\"cre\", \"aut\", \"cph\"), email = \"xll198708@gmail.com\"))", "Description": "A memory-efficient, visualize-enhanced,\nparallel-accelerated Genome-Wide Association Study (GWAS) tool.\nIt can (1) effectively process large data, (2) rapidly evaluate\npopulation structure, (3) efficiently estimate variance\ncomponents several algorithms, (4) implement\nparallel-accelerated association tests of markers three\nmethods, (5) globally efficient design on GWAS process\ncomputing, (6) enhance visualization of related information.\n'rMVP' contains three models GLM (Alkes Price (2006)\n), MLM (Jianming Yu (2006)\n) and FarmCPU (Xiaolei Liu (2016)\n); variance components\nestimation methods EMMAX (Hyunmin Kang (2008)\n;), FaSTLMM (method: Christoph\nLippert (2011) , R implementation from\n'GAPIT2': You Tang and Xiaolei Liu (2016)\n and 'SUPER': Qishan Wang and\nFeng Tian (2014) ), and HE\nregression (Xiang Zhou (2017) ).", "License": "Apache License 2.0", "Encoding": "UTF-8", "URL": "https://github.com/xiaolei-lab/rMVP", "BugReports": 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