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HDBRR
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", "1.1.4")
Attach the package and use:
library("HDBRR")
Maintained by
Blanca Monroy-Castillo Developer
[Scholar Profile | Author Map]
[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 1.1.4, https://cran.r-project.org/web/packages/HDBRR. Accessed 21 Nov. 2024.
Previous versions and publish date:
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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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