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cmaRs
View on CRAN: Click
here
Download and install cmaRs package within the R console
Install from CRAN:
install.packages("cmaRs")
Install from Github:
library("remotes")
install_github("cran/cmaRs") Install by package version:
library("remotes")
install_version("cmaRs", "0.1.4") Attach the package and use:
library("cmaRs")
Maintained by
Ceyda Yazici
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-22
Latest Update: 2023-07-04
Description:
An implementation of 'Conic Multivariate Adaptive Regression Splines (CMARS)' in R.
See Weber et al. (2011) CMARS: a new contribution to nonparametric regression with
multivariate adaptive regression splines supported by continuous optimization,
. It constructs models by using the terms
obtained from the forward step of MARS and then estimates parameters by using
'Tikhonov' regularization and conic quadratic optimization. It is possible to
construct models for prediction and binary classification. It provides performance
measures for the model developed. The package needs the optimisation software 'MOSEK'
to construct the models. Please follow the instructions in
'Rmosek' for the installation.
How to cite:
Ceyda Yazici (2021). cmaRs: Implementation of the Conic Multivariate Adaptive Regression Splines in R. R package version 0.1.4, https://cran.r-project.org/web/packages/cmaRs. Accessed 16 Jul. 2026.
Previous versions and publish date:
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Complete documentation for cmaRs
Functions, R codes and Examples using
the cmaRs R package
Some associated functions: classdata.std . cmaRs . plot.cmaRs . preddata.std . predict.cmaRs . summary.cmaRs . table.b6 .
Some associated R codes: class-data.R . cmaRs.R . cmaRs.derivative_more_than_one.R . cmaRs.derivative_one.R . cmaRs.fit.R . cmaRs.identical_function.R . cmaRs.mosek_optimization.R . plot.cmaRs.R . pred-data.R . predict.cmaRs.R . summary.cmaRs.R . tableb6-data.R . Full cmaRs package functions and examples
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