cmaRs: A powerful predictive data mining package in R

Yerlikaya-Özkurt F., Yazıcı C., BATMAZ İ.

SoftwareX, vol.24, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 24
  • Publication Date: 2023
  • Doi Number: 10.1016/j.softx.2023.101553
  • Journal Name: SoftwareX
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Keywords: Binary classification, Conic multivariate adaptive regression splines, Conic quadratic programming, Interior point method, Nonparametric regression, Tikhonov regularization
  • TED University Affiliated: No


Conic 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.