Package: crmReg 1.0.2

crmReg: Cellwise Robust M-Regression and SPADIMO

Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) <doi:10.1016/j.csda.2020.106944>) 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) <doi:10.1007/s11222-018-9831-5>).

Authors:Peter Filzmoser [aut], Sebastiaan Hoppner [aut, cre], Irene Ortner [aut], Sven Serneels [aut], Tim Verdonck [aut]

crmReg_1.0.2.tar.gz
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crmReg_1.0.2.tgz(r-4.4-any)crmReg_1.0.2.tgz(r-4.3-any)
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crmReg.pdf |crmReg.html
crmReg/json (API)

# Install 'crmReg' in R:
install.packages('crmReg', repos = c('https://sebastiaanhoppner.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.04 score 11 scripts 168 downloads 5 exports 41 dependencies

Last updated 4 years agofrom:29f4e61d1b. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 16 2024
R-4.5-winOKOct 16 2024
R-4.5-linuxOKOct 16 2024
R-4.4-winOKOct 16 2024
R-4.4-macOKOct 16 2024
R-4.3-winOKOct 16 2024
R-4.3-macOKOct 16 2024

Exports:cellwiseheatmapcrmdaprprpredict.crmspadimo

Dependencies:bitopscaToolsclicolorspaceDEoptimRfansifarverFNNggplot2gluegplotsgtablegtoolsisobandKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellmvtnormnlmepcaPPpillarpkgconfigplyrR6RColorBrewerRcpprlangrobustbaserrcovscalestibbleutf8vctrsviridisLitewithr