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bark (version 1.0.5)

Bayesian Additive Regression Kernels

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 .

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Install

install.packages('bark')

Monthly Downloads

376

Version

1.0.5

License

GPL (>= 3)

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Last Published

October 5th, 2024

Functions in bark (1.0.5)

sim.Friedman2-deprecated

Simulated Regression Problem Friedman 2
sim_Friedman1

Simulated Regression Problem Friedman 1
bark

Nonparametric Regression using Bayesian Additive Regression Kernels
bark-package

bark: Bayesian Additive Regression Trees
sim_Friedman3

Simulated Regression Problem Friedman 3
sim_Friedman2

Simulated Regression Problem Friedman 2
sim_circle

Simulate Data from Hyper-Sphere for Classification Problems
banknotes

Swiss Bank Notes
bark-package-deprecated

Deprecated functions in package bark.
sim.Circle-deprecated

Simulate Data from Hyper-Sphere for Classification Problems
sim.Friedman1-deprecated

Simulated Regression Problem Friedman 1
sim.Friedman3-deprecated

Simulated Regression Problem Friedman 3
bark-deprecated

NonParametric Regression using Bayesian Additive Regression Kernels