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bark  

Bayesian Additive Regression Kernels
View on CRAN: Click here


Download and install bark package within the R console
Install from CRAN:
install.packages("bark")

Install from Github:
library("remotes")
install_github("cran/bark")

Install by package version:
library("remotes")
install_version("bark", "1.0.5")



Attach the package and use:
library("bark")
Maintained by
Merlise Clyde
[Scholar Profile | Author Map]
First Published: 2008-07-16
Latest Update: 2023-04-18
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 .
How to cite:
Merlise Clyde (2008). bark: Bayesian Additive Regression Kernels. R package version 1.0.5, https://cran.r-project.org/web/packages/bark. Accessed 13 May. 2025.
Previous versions and publish date:
0.1-0 (2008-07-16 18:42), 1.0.1 (2023-03-09 10:40), 1.0.4 (2023-04-18 21:10)
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