bark: Bayesian Additive Regression Kernels
Bayesian Additive Regression Kernels (BARK) providesan implementation for non-parametric function estimation using LevyRandom Field priors for functions that may be represented as asum of additive multivariate kernels. Kernels are located atevery 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 onprecision 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 LevyAdaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 <doi:10.1214/11-AOS889>.
| Version: | 1.0.5 |
| Depends: | R (≥ 3.5.0) |
| Suggests: | BART,e1071,fdm2id,rmarkdown,knitr,roxygen2,testthat,covr |
| Published: | 2024-10-05 |
| 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 duke.edu> |
| BugReports: | https://github.com/merliseclyde/bark/issues |
| License: | GPL (≥ 3) |
| URL: | https://www.R-project.org,https://github.com/merliseclyde/bark |
| NeedsCompilation: | yes |
| Language: | en-US |
| Materials: | NEWS |
| CRAN checks: | bark results |
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