{ "_id": "6a1571f1acfb0bcc41d6538b", "Package": "HhP", "Title": "Hierarchical Heterogeneity Analysis via Penalization", "Version": "1.0.0", "Authors@R": "c(person(\"Mingyang\", \"Ren\", email = \"renmingyang17@mails.ucas.ac.cn\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-8061-9940\")),\nperson(\"Qingzhao\", \"Zhang\", role = c(\"aut\")),\nperson(\"Sanguo\", \"Zhang\", role = c(\"aut\")),\nperson(\"Tingyan\", \"Zhong\", role = c(\"aut\")),\nperson(\"Jian\", \"Huang\", role = c(\"aut\")),\nperson(\"Shuangge\", \"Ma\", role = c(\"aut\")))", "Description": "In medical research, supervised heterogeneity analysis has\nimportant implications. Assume that there are two types of\nfeatures. Using both types of features, our goal is to conduct\nthe first supervised heterogeneity analysis that satisfies a\nhierarchical structure. That is, the first type of features\ndefines a rough structure, and the second type defines a nested\nand more refined structure. A penalization approach is\ndeveloped, which has been motivated by but differs\nsignificantly from penalized fusion and sparse group\npenalization. Reference: Ren, M., Zhang, Q., Zhang, S., Zhong,\nT., Huang, J. & Ma, S. (2022). \"Hierarchical cancer\nheterogeneity analysis based on histopathological imaging\nfeatures\". 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