Learn R Programming

cellWise (version 2.5.7)

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".

Copy Link

Version

Install

install.packages('cellWise')

Monthly Downloads

3,287

Version

2.5.7

License

GPL (>= 2)

Maintainer

Jakob Raymaekers

Last Published

February 26th, 2026

Functions in cellWise (2.5.7)

Estimate location and scatter of data with cellwise weights

The mortality dataset

The clothes dataset

transfo_transformback

Backtransform variables based on the output of transfo.

transfo_newdata

Transform variables based on the output of transfo.

Estimate robust location and scale

Generates correlation matrices

Unpacks cellwise weighted data

Estimates location and scatter on incomplete data with case weights

Classical Principal Components by truncated SVD.

Draw plots based on the cellwise minimum covariance determinant estimator cellMCD

Robustly fit the Box-Cox or Yeo-Johnson transformation

Generates artificial datasets with outliers

Plot the outlier map.

cellHandler algorithm

Detect Deviating Cells

Detection-Imputation algorithm

Clean the dataset

Iterative Classical PCA

cellWise minimum covariance determinant estimator

MacroPCApredict

MacroPCApredict

Dog walker dataset

data_personality_traits

The personality traits data

The philips dataset

The glass dataset

The brands dataset