{ "_id": "6a2143d7cd65a98ecbd2af11", "Package": "iSFun", "Type": "Package", "Title": "Integrative Dimension Reduction Analysis for Multi-Source Data", "Version": "1.1.0", "Authors@R": "c(person(\"Kuangnan\", \"Fang\", role=c(\"aut\")),\nperson(\"Rui\", \"Ren\", role=c(\"aut\", \"cre\"),\nemail = \"xmurr@stu.xmu.edu.cn\"),\nperson(\"Qingzhao\", \"Zhang\", role=c(\"aut\")),\nperson(\"Shuangge\", \"Ma\", role=c(\"aut\")))", "Maintainer": "Rui Ren ", "Description": "The implement of integrative analysis methods based on a\ntwo-part penalization, which realizes dimension reduction\nanalysis and mining the heterogeneity and association of\nmultiple studies with compatible designs. The software package\nprovides the integrative analysis methods including integrative\nsparse principal component analysis (Fang et al., 2018),\nintegrative sparse partial least squares (Liang et al., 2021)\nand integrative sparse canonical correlation analysis, as well\nas corresponding individual analysis and meta-analysis\nversions. References: (1) Fang, K., Fan, X., Zhang, Q., and Ma,\nS. (2018). Integrative sparse principal component analysis.\nJournal of Multivariate Analysis,\n. (2) Liang, W., Ma, S., Zhang,\nQ., and Zhu, T. (2021). Integrative sparse partial least\nsquares. 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