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HDBRR  

High Dimensional Bayesian Ridge Regression without MCMC
View on CRAN: Click here


Download and install HDBRR package within the R console
Install from CRAN:
install.packages("HDBRR")

Install from Github:
library("remotes")
install_github("cran/HDBRR")

Install by package version:
library("remotes")
install_version("HDBRR", "1.1.5")



Attach the package and use:
library("HDBRR")
Maintained by
Blanca Monroy-Castillo Developer
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-04-20
Latest Update: 2022-10-05
Description:
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.
How to cite:
Blanca Monroy-Castillo Developer (2021). HDBRR: High Dimensional Bayesian Ridge Regression without MCMC. R package version 1.1.5, https://cran.r-project.org/web/packages/HDBRR. Accessed 07 Oct. 2026.
Previous versions and publish date:
0.1.8 (2021-04-20 09:50), 1.1.0 (2021-08-13 18:20), 1.1.1 (2021-09-22 23:20), 1.1.2 (2021-10-21 06:20), 1.1.3 (2021-11-07 09:00), 1.1.4 (2022-10-06 01:10), (2026-09-06 09:40)
Other packages that cited HDBRR R package
View HDBRR citation profile
Other R packages that HDBRR depends, imports, suggests or enhances
Complete documentation for HDBRR
Functions, R codes and Examples using the HDBRR R package
Some associated functions: HDBRR . X . matop . pheno . phenowheat . 
Some associated R codes: HDBRR.R . X.R . coef.HDBRR.R . pheno.R . phenowheat.R . plot.HDBRR.R . predict.HDBRR.R . print.HDBRR.R . print.summary.HDBRR.R . summary.HDBRR.R .  Full HDBRR package functions and examples
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