covdepGE
Covariate Dependent Graph Estimation
A covariate-dependent approach to Gaussian graphical modeling as described in Dasgupta et al. (2022). Employs a novel weighted pseudo-likelihood approach to model the conditional dependence structure of data as a continuous function of an extraneous covariate. The main function, covdepGE::covdepGE(), estimates a graphical representation of the conditional dependence structure via a block mean-field variational approximation, while several auxiliary functions (inclusionCurve(), matViz(), and plot.covdepGE()) are included for visualizing the resulting estimates.
- Version1.0.1
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Languageen-US
- Last release09/16/2022
Documentation
Team
Jacob Helwig
Peng Zhao
Show author detailsRolesAuthorDebdeep Pati
Show author detailsRolesAuthorSutanoy Dasgupta
Show author detailsRolesAuthorBani Mallick
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 172 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,280 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Jul 23, 2024 with 23 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports8 packages
- Suggests3 packages
- Linking To2 packages