Package: cellWise 2.5.7

Jakob Raymaekers

cellWise: Analyzing Data with Cellwise Outliers

Tools for detecting cellwise outliers and robust methods to analyze data which may contain them. Contains the implementation of the algorithms described in Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909> (open access) Hubert et al. (2019) <doi:10.1080/00401706.2018.1562989> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1080/00401706.2019.1677270> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5> (open access), Raymaekers and Rousseeuw (2021) <doi:10.52933/jdssv.v1i3.18> (open access), Raymaekers and Rousseeuw (2022) <doi:10.1080/01621459.2023.2267777> (open access) Rousseeuw (2022) <doi:10.1016/j.ecosta.2023.01.007> (open access). Examples can be found in the vignettes: "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples", "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples", and "cellwise_weights_examples".

Authors:Jakob Raymaekers [aut, cre], Peter Rousseeuw [aut], Wannes Van den Bossche [ctb], Mia Hubert [ctb]

cellWise_2.5.7.tar.gz
cellWise_2.5.7.tar.gz(r-4.7-arm64)cellWise_2.5.7.tar.gz(r-4.7-x86_64)cellWise_2.5.7.tar.gz(r-4.6-arm64)cellWise_2.5.7.tar.gz(r-4.6-x86_64)
cellWise_2.5.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
cellWise/json (API)

# Install 'cellWise' in R:
install.packages('cellWise', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

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

openblascppopenmp

7.21 score 2 stars 18 packages 75 scripts 4.1k downloads 2 mentions 23 exports 34 dependencies

Last updated from:f6e62abd20. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK373
linux-devel-x86_64OK398
source / vignettesOK628
linux-release-arm64OK360
linux-release-x86_64OK368
wasm-releaseOK457

Exports:cellHandlercellMapcellMCDcheckDataSetcwLocScatDDCDDCpredictDIestLocScalegenerateCorMatgenerateDataICPCAMacroPCAMacroPCApredictoutlierMapplot_cellMCDtransfotransfo_newdatatransfo_transformbacktruncPCunpackweightedEMwrap

Dependencies:clicpp11DEoptimRfarverggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagrittrmatrixStatsmvtnormpcaPPplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangrobustbaserrcovS7scalesshapestringistringrsvdvctrsviridisLitewithr

cellMCD examples
Introduction | Top Gear data | Comparison of cellMCD with covMCD | Aircraft data without response (n=23, d=4) | Aircraft with response (n=23, d=5) | alcohol (n=44, d=7) | Animals2 (n=65, d=2) | bushfire (n=38, d=5) | cloud (n = 19, d = 2) | delivery without response n=25, d=2 | delivery with response n=25, d=3 | exAM (n=12, d=2) | hbk without response (n=75, d=3) | hbk with response (n=75, d=4) | kootenay (n=13, d=2) | lactic (n=13, d=2) | milk (n=68, d=8) | pension (n=13, d=2) | phosphor (n=18, d=3) | pilot (n=20, d=2) | radarImage n=1573, d=5 | Salinity without response n=28, d=3 | Salinity with response (n=28, d=4) | starsCYG (n=47, d=2) | toxicity (n=38, d=10) | wood without response n=20, d=5
Rendered fromcellMCD_examples.Rmdusingknitr::knitr

Last update: 2023-10-25
Started: 2022-08-10

cellwise weights examples
Introduction | Unpack the toy example in section 2 of the paper | Playing with the function cwLocScat | Personality traits example from section 4
Rendered fromcellwise_weights_examples.Rmdusingknitr::knitr

Last update: 2023-10-25
Started: 2022-12-11

Correspondence analysis examples
Introduction | Clothes data | Brand perception example
Rendered fromCorrespondence_analysis_examples.Rmdusingknitr::knitr

Last update: 2023-10-25
Started: 2022-12-11

DDC examples
Introduction | Example with row and column selection | Small generated dataset | TopGear dataset | Analyzing new data by DDCpredict | Define the "initial" dataset as the rows not in these 17: | Define the "new" dataset, and apply DDCpredict to it: | Philips data | We also apply the rowwise method MCD to detect outlying rows: | Mortality dataset | We also apply the rowwise method ROBPCA to detect outlying rows: | Glass dataset | We will compare this with the faster approximate algorithm of DDC, obtained by the option fastDDC=TRUE:
Rendered fromDDC_examples.Rmdusingknitr::knitr

Last update: 2023-10-25
Started: 2016-12-07

DI examples
Introduction | Artificial data | VOC data
Rendered fromDI_examples.Rmdusingknitr::knitr

Last update: 2023-10-25
Started: 2020-12-03

MacroPCA examples
Introduction | Small generated example | TopGear dataset | Also run ICPCA (iterative classical PCA): | TopGear dataset: prediction of new data | Glass dataset | We now compare MacroPCA with ROBPCA: | We now compare fastDDC=FALSE with fastDDC=TRUE in MacroPCA: | DPOSS dataset
Rendered fromMacroPCA_examples.Rmdusingknitr::knitr

Last update: 2023-10-25
Started: 2019-02-25

transfo examples
Introduction | Small toy example | Transform new data and transform back | TopGear example | Glass data example | DPOSS data example
Rendered fromtransfo_examples.Rmdusingknitr::knitr

Last update: 2023-10-25
Started: 2020-11-11

wrap examples
Introduction | Dog walker video example
Rendered fromwrap_examples.Rmdusingknitr::knitr

Last update: 2023-10-25
Started: 2021-03-09