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rgnoisefilt
View on CRAN: Click
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Download and install rgnoisefilt package within the R console
Install from CRAN:
install.packages("rgnoisefilt")
Install from Github:
library("remotes")
install_github("cran/rgnoisefilt") Install by package version:
library("remotes")
install_version("rgnoisefilt", "1.1.3") Attach the package and use:
library("rgnoisefilt")
Maintained by
Juan Martin
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-09-13
Latest Update: 2025-05-23
Description:
Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques for use in regression problems, and it also incorporates methods specifically designed for regression data. In order to do this, it uses approaches proposed in the specialized literature, such as Martin et al. (2021) [] and Arnaiz-Gonzalez et al. (2016) []. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.
How to cite:
Juan Martin (2023). rgnoisefilt: Elimination of Noisy Samples in Regression Datasets using Noise Filters. R package version 1.1.3, https://cran.r-project.org/web/packages/rgnoisefilt. Accessed 10 Mar. 2026.
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Complete documentation for rgnoisefilt
Functions, R codes and Examples using
the rgnoisefilt R package
Some associated functions: discCNN . discENN . discNCL . discTL . plot.rfdata . print.rfdata . regAENN . regBBNR . regCNN . regCVCF . regDF . regEF . regENN . regFMF . regGE . regHRRF . regIPF . regIRF . regRND . regRNN . rfCDF . rfDROP2 . rfDROP3 . rfMIF . summary.rfdata .
Some associated R codes: discCNN.R . discENN.R . discNCL.R . discTL.R . regAENN.R . regBBNR.R . regCNN.R . regCVCF.R . regDF.R . regEF.R . regENN.R . regFMF.R . regGE.R . regHRRF.R . regIPF.R . regIRF.R . regRND.R . regRNN.R . rfCDF.R . rfDROP2.R . rfDROP3.R . rfMIF.R . rfdata.R . utility.R . Full rgnoisefilt package functions and examples
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