Package: bark Type: Package Title: Bayesian Additive Regression Kernels Version: 1.0.6 Date: 2024-10-05 Authors@R: c(person("Merlise", "Clyde", email="clyde@duke.edu", role=c("aut","cre", "ths"), comment=c("ORCID=0000-0002-3595-1872") ), person("Zhi", "Ouyang", email="zhi.ouyang@gmail.com", role=c("aut")), person("Robert", "Wolpert", email="wolpert@stat.duke.edu", role=c("ctb", "ths")) ) Description: 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 . License: GPL (>= 3) URL: https://www.R-project.org, https://github.com/merliseclyde/bark BugReports: https://github.com/merliseclyde/bark/issues Depends: R (>= 3.5.0) Suggests: BART, e1071, fdm2id, rmarkdown, knitr, roxygen2, testthat, covr LazyData: yes NeedsCompilation: yes ByteCompile: yes Encoding: UTF-8 RoxygenNote: 7.3.2 Language: en-US VignetteBuilder: knitr Repository: https://merliseclyde.r-universe.dev Date/Publication: 2024-10-06 04:43:45 UTC RemoteUrl: https://github.com/merliseclyde/bark RemoteRef: HEAD RemoteSha: 3da96e751b6df6cd55038a5458c1b86ac4471b43 Packaged: 2026-06-15 11:56:48 UTC; root Author: Merlise Clyde [aut, cre, ths] (ORCID=0000-0002-3595-1872), Zhi Ouyang [aut], Robert Wolpert [ctb, ths] Maintainer: Merlise Clyde