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crmReg
View on CRAN: Click
here
Download and install crmReg package within the R console
Install from CRAN:
install.packages("crmReg")
Install from Github:
library("remotes")
install_github("cran/crmReg") Install by package version:
library("remotes")
install_version("crmReg", "1.0.4") Attach the package and use:
library("crmReg")
Maintained by
Sebastiaan Hoppner
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All associated links for this package
First Published: 2020-03-31
Latest Update: 2025-05-22
Description:
Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) ) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) ).
How to cite:
Sebastiaan Hoppner (2020). crmReg: Cellwise Robust M-Regression and SPADIMO. R package version 1.0.4, https://cran.r-project.org/web/packages/crmReg. Accessed 15 Jul. 2026.
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Complete documentation for crmReg
Functions, R codes and Examples using
the crmReg R package
Some associated functions: cellwiseheatmap . crm . crmReg-package . daprpr . predict.crm . spadimo . topgear .
Some associated R codes: HampelWeightFunction.R . cellwiseheatmap.R . crm.R . daprpr.R . impute_outlying_cells.R . predict.crm.R . scaleResidualsByMAD.R . spadimo.R . Full crmReg package functions and examples
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