cvCovEst
Cross-Validated Covariance Matrix Estimation
An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) doi:10.1080/10618600.2022.2110883 to identify the optimal estimator from among a prespecified set of candidates.
- Version1.2.2
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Languageen-US
- cvCovEst citation info
- Last release02/17/2024
Documentation
Team
Philippe Boileau
Nima Hejazi
Show author detailsRolesAuthorMark van der Laan
Show author detailsRolesContributor, Thesis advisorBrian Collica
Jamarcus Liu
Show author detailsRolesContributorSandrine Dudoit
Insights
Last 30 days
This package has been downloaded 344 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 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 4,452 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 51 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
- Imports16 packages
- Suggests8 packages
- Reverse Imports2 packages
- Reverse Suggests1 package