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HhP  

Hierarchical Heterogeneity Analysis via Penalization
View on CRAN: Click here


Download and install HhP package within the R console
Install from CRAN:
install.packages("HhP")

Install from Github:
library("remotes")
install_github("cran/HhP")

Install by package version:
library("remotes")
install_version("HhP", "1.0.0")



Attach the package and use:
library("HhP")
Maintained by
Mingyang Ren
[Scholar Profile | Author Map]
First Published: 2022-11-23
Latest Update: 2022-11-23
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, .
How to cite:
Mingyang Ren (2022). HhP: Hierarchical Heterogeneity Analysis via Penalization. R package version 1.0.0, https://cran.r-project.org/web/packages/HhP. Accessed 08 Apr. 2025.
Previous versions and publish date:
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Complete documentation for HhP
Functions, R codes and Examples using the HhP R package
Some associated functions: HhP.reg . evaluation.sum . example.data.GGM . example.data.reg . gen_int_beta . genelambdao . 
Some associated R codes: HhP.reg.R . evaluation.funs.R . evaluation.sum.R . example.data.GGM.R . example.data.reg.R . gen_int_beta.R . genelambdao.R . iter_functions.R . support_functions.R .  Full HhP package functions and examples
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