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cellWise (version 2.5.4)

Analyzing Data with Cellwise Outliers

Description

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) (open access) Hubert et al. (2019) (open access), Raymaekers and Rousseeuw (2021) (open access), Raymaekers and Rousseeuw (2021) (open access), Raymaekers and Rousseeuw (2021) (open access), Raymaekers and Rousseeuw (2022) (open access) Rousseeuw (2022) (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".

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Version

Install

install.packages('cellWise')

Monthly Downloads

8,658

Version

2.5.4

License

GPL (>= 2)

Maintainer

Jakob Raymaekers

Last Published

September 23rd, 2025

Functions in cellWise (2.5.4)

Detect Deviating Cells

The brands dataset

Estimate location and scatter of data with cellwise weights

The philips dataset

data_personality_traits

The personality traits data

Dog walker dataset

The mortality dataset

The glass dataset

MacroPCApredict

MacroPCApredict

Generates artificial datasets with outliers

transfo_newdata

Transform variables based on the output of transfo.

Plot the outlier map.

Unpacks cellwise weighted data

transfo_transformback

Backtransform variables based on the output of transfo.

Classical Principal Components by truncated SVD.

Generates correlation matrices

Draw plots based on the cellwise minimum covariance determinant estimator cellMCD

Estimate robust location and scale

Robustly fit the Box-Cox or Yeo-Johnson transformation

The clothes dataset

Estimates location and scatter on incomplete data with case weights

Iterative Classical PCA

Detection-Imputation algorithm

cellWise minimum covariance determinant estimator

cellHandler algorithm

Clean the dataset