bark: Bayesian Additive Regression Kernels

Bayesian Additive Regression Kernels (BARK) provides an implementation for non-parametric function estimation using Levy Random Field priors for functions that may be represented as a sum of additive multivariate kernels. Kernels are located at every data point as in Support Vector Machines, however, coefficients may be heavily shrunk to zero under the Cauchy process prior, or even, set to zero. The number of active features is controlled by priors on precision parameters within the kernels, permitting feature selection. For more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels", Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 <doi:10.1214/11-AOS889>.

Version: 1.0.4
Suggests: BART, e1071, rmarkdown, knitr, roxygen2, testthat, covr
Published: 2023-04-18
DOI: 10.32614/CRAN.package.bark
Author: Merlise Clyde [aut, cre, ths] (ORCID=0000-0002-3595-1872), Zhi Ouyang [aut], Robert Wolpert [ctb, ths]
Maintainer: Merlise Clyde <clyde at>
License: GPL (≥ 3)
NeedsCompilation: yes
Language: en-US
Materials: NEWS
CRAN checks: bark results


Reference manual: bark.pdf
Vignettes: Nonparametric Regression with Bayesian Additive Regression Kernels


Package source: bark_1.0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bark_1.0.4.tgz, r-oldrel (arm64): bark_1.0.4.tgz, r-release (x86_64): bark_1.0.4.tgz, r-oldrel (x86_64): bark_1.0.4.tgz
Old sources: bark archive


Please use the canonical form to link to this page.