Package: HDBRR 1.1.4
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.
Authors:
HDBRR_1.1.4.tar.gz
HDBRR_1.1.4.zip(r-4.5)HDBRR_1.1.4.zip(r-4.4)HDBRR_1.1.4.zip(r-4.3)
HDBRR_1.1.4.tgz(r-4.4-any)HDBRR_1.1.4.tgz(r-4.3-any)
HDBRR_1.1.4.tar.gz(r-4.5-noble)HDBRR_1.1.4.tar.gz(r-4.4-noble)
HDBRR_1.1.4.tgz(r-4.4-emscripten)HDBRR_1.1.4.tgz(r-4.3-emscripten)
HDBRR.pdf |HDBRR.html✨
HDBRR/json (API)
# Install 'HDBRR' in R: |
install.packages('HDBRR', repos = c('https://castlemon.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:b672266198. Checks:OK: 3 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 15 2024 |
R-4.5-win | NOTE | Nov 15 2024 |
R-4.5-linux | NOTE | Nov 15 2024 |
R-4.4-win | NOTE | Nov 15 2024 |
R-4.4-mac | NOTE | Nov 15 2024 |
R-4.3-win | OK | Nov 15 2024 |
R-4.3-mac | OK | Nov 15 2024 |
Exports:coef.HDBRRHDBRRmatopplot.HDBRRpredict.HDBRRprint.HDBRRprint.summary.HDBRRsummary.HDBRR
Dependencies:bigassertrbigparallelrbigstatsrbitclicodetoolscolorspacecowplotdoParallelfansifarverffflockforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmenumDerivparallellypillarpkgconfigpsR6RColorBrewerRcppRcppArmadilloRcppEigenRhpcBLASctlrlangrmioRSpectrascalestibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
High Dimensional Bayesian Ridge Regression without MCMC. | coef.HDBRR HDBRR plot.HDBRR predict.HDBRR print.HDBRR print.summary.HDBRR summary.HDBRR |
matop | matop |
Durum Wheat | pheno |
Durum wheat dataset | phenowheat |
Durum Wheat X | X |