HDBRR
High Dimensional Bayesian Ridge Regression without MCMC
Ridge regression provide biased estimators of the regression parameters with lower variance. The HDBRR ("High Dimensional Bayesian Ridge Regression") function fits Bayesian Ridge regression without MCMC, this one uses the SVD or QR decomposition for the posterior computation.
- Version1.1.4
- R version≥ 3.0.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release10/05/2022
Documentation
Team
Blanca Monroy-Castillo
Sergio Perez-Elizalde
Show author detailsRolesAuthorPaulino Perez-Rodriguez
Show author detailsRolesContributorJose Crossa
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 157 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 4 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 2,913 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 Jul 21, 2024 with 140 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
- Imports3 packages