Package: HhP 1.0.0
Mingyang Ren
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>.
Authors:
HhP_1.0.0.tar.gz
HhP_1.0.0.tar.gz(r-4.5-noble)HhP_1.0.0.tar.gz(r-4.4-noble)
HhP_1.0.0.tgz(r-4.4-emscripten)HhP_1.0.0.tgz(r-4.3-emscripten)
HhP.pdf |HhP.html✨
HhP/json (API)
# Install 'HhP' in R: |
install.packages('HhP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- example.data.GGM - Some example data
- example.data.reg - Some example data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:a36e7768ee. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 25 2024 |
R-4.5-linux | OK | Nov 25 2024 |
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Hierarchical Heterogeneity Regression Analysis. | evaluation.sum |
Some example data | example.data.GGM |
Some example data | example.data.reg |
Hierarchical Heterogeneity Regression Analysis. | gen_int_beta |
Generate tuning parameters | genelambda.obo |
Hierarchical Heterogeneity Regression Analysis. | HhP.reg |