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"),
           comment = c(ORCID = "0000-0002-8061-9940")),
      person("Qingzhao", "Zhang", role = c("aut")),
      person("Sanguo", "Zhang", role = c("aut")),
      person("Tingyan", "Zhong", role = c("aut")),
      person("Jian", "Huang", role = c("aut")),
      person("Shuangge", "Ma", role = c("aut")))
Description: In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. 
             Reference: 
             Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.
License: GPL-2
Encoding: UTF-8
Imports: MASS, Matrix, fmrs, methods
LazyData: true
LazyLoad: yes
RoxygenNote: 7.1.2
Depends: R (>= 3.5.0)
Suggests: knitr, rmarkdown
VignetteBuilder: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2022-11-22 11:27:35 UTC; 10259
Author: Mingyang Ren [aut, cre] (<https://orcid.org/0000-0002-8061-9940>),
  Qingzhao Zhang [aut],
  Sanguo Zhang [aut],
  Tingyan Zhong [aut],
  Jian Huang [aut],
  Shuangge Ma [aut]
Maintainer: Mingyang Ren <renmingyang17@mails.ucas.ac.cn>
Repository: CRAN
Date/Publication: 2022-11-23 11:30:11 UTC
Built: R 4.6.0; ; 2026-04-26 00:54:58 UTC; unix
