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:Sergio Perez-Elizalde Developer [aut], Blanca Monroy-Castillo Developer [aut, cre], Paulino Perez-Rodriguez User [ctb], Jose Crossa User [ctb]

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)

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

8 exports 0.00 score 48 dependencies 1 scripts 258 downloads

Last updated 2 years agofrom:b672266198. Checks:OK: 3 NOTE: 4. Indexed: yes.

Exports:coef.HDBRRHDBRRmatopplot.HDBRRpredict.HDBRRprint.HDBRRprint.summary.HDBRRsummary.HDBRR

Dependencies:bigassertrbigparallelrbigstatsrbitclicodetoolscolorspacecowplotdoParallelfansifarverffflockforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmenumDerivparallellypillarpkgconfigpsR6RColorBrewerRcppRcppArmadilloRcppEigenRhpcBLASctlrlangrmioRSpectrascalestibbleutf8vctrsviridisLitewithr