| Name : R-HeteroGGM
| |
| Version : 1.0.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.2.4
| Date : 2024-06-14 11:17:20
|
| Group : Development/Libraries/Other
| Source RPM : R-HeteroGGM-1.0.1-lp153.2.4.src.rpm
|
| Size : 0.36 MB
| |
| Packager : (none)
| |
| Summary : Gaussian Graphical Model-Based Heterogeneity Analysis
|
Description :
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
