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  "Authors@R": "c(person(\"Merlise\", \"Clyde\", email=\"clyde@duke.edu\",\nrole=c(\"aut\",\"cre\", \"ths\"),\ncomment=c(\"ORCID=0000-0002-3595-1872\")\n),\nperson(\"Zhi\", \"Ouyang\", email=\"zhi.ouyang@gmail.com\",\nrole=c(\"aut\")),\nperson(\"Robert\", \"Wolpert\", email=\"wolpert@stat.duke.edu\",\nrole=c(\"ctb\", \"ths\"))\n)",
  "Description": "Bayesian Additive Regression Kernels (BARK) provides an\nimplementation for non-parametric function estimation using\nLevy Random Field priors for functions that may be represented\nas a sum of additive multivariate kernels.  Kernels are located\nat every data point as in Support Vector Machines, however,\ncoefficients may be heavily shrunk to zero under the Cauchy\nprocess prior, or even, set to zero.  The number of active\nfeatures is controlled by priors on precision parameters within\nthe kernels, permitting feature selection. For more details see\nOuyang, Z (2008) \"Bayesian Additive Regression Kernels\", Duke\nUniversity. PhD dissertation, Chapter 3 and Wolpert, R. L,\nClyde, M.A, and Tu, C. (2011) \"Stochastic Expansions with\nContinuous Dictionaries Levy Adaptive Regression Kernels,\nAnnals of Statistics Vol (39) pages 1916-1962\n<doi:10.1214/11-AOS889>.",
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