HhP: Hierarchical Heterogeneity Analysis via Penalization

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>.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: MASS, Matrix, fmrs, methods
Suggests: knitr, rmarkdown
Published: 2022-11-23
Author: Mingyang Ren ORCID iD [aut, cre], Qingzhao Zhang [aut], Sanguo Zhang [aut], Tingyan Zhong [aut], Jian Huang [aut], Shuangge Ma [aut]
Maintainer: Mingyang Ren <renmingyang17 at mails.ucas.ac.cn>
License: GPL-2
NeedsCompilation: no
CRAN checks: HhP results


Reference manual: HhP.pdf
Vignettes: HhP


Package source: HhP_1.0.0.tar.gz
Windows binaries: r-devel: HhP_1.0.0.zip, r-release: HhP_1.0.0.zip, r-oldrel: HhP_1.0.0.zip
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available


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