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cmaRs: A powerful predictive data mining package in R

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dc.contributor.authorYerlikaya-Özkurt, Fatma
dc.contributor.authorYazıcı, Ceyda
dc.contributor.authorBatmaz, İnci
dc.date.accessioned2023-12-22T11:05:49Z
dc.date.available2023-12-22T11:05:49Z
dc.date.issued2023-12-15
dc.descriptionOpen Access, Published by SoftwareX, https://doi.org/10.1016/j.softx.2023.101553, Fatma Yerlikaya-Özkurt, Department of Industrial Engineering, Atılım University, Ankara, Turkey, Ceyda Yazıcı, Department of Mathematics, TED University, Ankara, Turkey, İnci Batmaz, Department of Statistics, Middle East Technical University, Ankara, Turkey.
dc.description.abstractConic Multivariate Adaptive Regression Splines (CMARS) is a very successful method for modeling nonlinear structures in high-dimensional data. It is based on MARS algorithm and utilizes Tikhonov regularization and Conic Quadratic Optimization (CQO). In this paper, the open-source R package, cmaRs, built to construct CMARS models for prediction and binary classification is presented with illustrative applications. Also, the CMARS algorithm is provided in both pseudo and R code. Note here that cmaRs package provides a good example for a challenging implementation of CQO based on MOSEK solver in R environment by linking R to MOSEK through the package Rmosek.
dc.identifier.citationhttp://hdl.handle.net/20.500.14411/1927
dc.identifier.issn2352-7110
dc.identifier.urihttps://doi.org/10.1016/j.softx.2023.101553
dc.language.isoen
dc.publisherSoftwareX
dc.relation.ispartofseries24
dc.subjectConic multivariate adaptive regression splines, Nonparametric regression, Tikhonov regularization, Conic quadratic programming, Interior point method, Binary classification
dc.titlecmaRs: A powerful predictive data mining package in R
dc.typeArticle
dspace.entity.typeArticle

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