The goal of this package is to user-friendly realizing Gaussian graphical model-based heterogeneity analysis. Recently, several Gaussian graphical model-based heterogeneity analysis techniques have been developed. A common methodological limitation is that the number of subgroups is assumed to be known a priori, which is not realistic. In a very recent study (Ren et al., 2022), a novel approach based on the penalized fusion technique is developed to fully data-dependently determine the number and structure of subgroups in Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing the Gaussian graphical model technique in more practical settings. Beyond Ren et al. (2022), more estimations and functions are added, so that the package is self-contained and more comprehensive and can provide ``more direct'' insights to practitioners (with the visualization function). Reference: Ren, M., Zhang S., Zhang Q. and Ma S. (2022). Gaussian Graphical Model-based Heterogeneity Analysis via Penalized Fusion. Biometrics, 78 (2), 524-535.
Package details |
|
---|---|
Author | Mingyang Ren [aut, cre] (<https://orcid.org/0000-0002-8061-9940>), Sanguo Zhang [aut], Qingzhao Zhang [aut], Shuangge Ma [aut] |
Maintainer | Mingyang Ren <renmingyang17@mails.ucas.ac.cn> |
License | GPL-2 |
Version | 1.0.1 |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.