2 10 11 https://repository.horizon.ac.id/files/original/18946071c1e9443e150b8cffdd43b6f0.pdf 2b17db93329a069a406fd012cfbe7553 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 97 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v097 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar Bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Indonesia Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package Subject The topic of the resource : binary trees, black-box, categorical, competing risks, continuous, ensemble predictive model, forking, multinomial, multi-threading, OpenMP, recurrent events, survival analysis. Description An account of the resource In this article, we introduce the BART R package which is an acronym for Bayesian additive regression trees. BART is a Bayesian nonparametric, machine learning, ensemble predictive modeling method for continuous, binary, categorical and time-to-event outcomes. Furthermore, BART is a tree-based, black-box method which fits the outcome to an arbitrary random function, f, of the covariates. The BART technique is relatively computationally efficient as compared to its competitors, but large sample sizes can be demanding. Therefore, the BART package includes efficient state-of-the-art implementations for continuous, binary, categorical and time-to-event outcomes that can take advantage of modern off-the-shelf hardware and software multi-threading technology. The BART package is written in C++ for both programmer and execution efficiency. The BART package takes advantage of multi-threading via forking as provided by the parallel package and OpenMP when available and supported by the platform. The ensemble of binary trees produced by a BART fit can be stored and re-used later via the R predict function. In addition to being an R package, the installed BART routines can be called directly from C++. The BART package provides the tools for your BART toolbox. Creator An entity primarily responsible for making the resource Rodney Sparapani Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v097i01 Publisher An entity responsible for making the resource available Medical College of Wisconsin Date A point or period of time associated with an event in the lifecycle of the resource Januari 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Inggris Type The nature or genre of the resource Text