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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 |
DOI: | 10.32614/CRAN.package.HhP |
Author: | Mingyang Ren |
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): HhP_1.0.0.tgz, r-oldrel (arm64): HhP_1.0.0.tgz, r-release (x86_64): HhP_1.0.0.tgz, r-oldrel (x86_64): HhP_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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