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cellWise (version 2.5.3)

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

1,086

Version

2.5.3

License

GPL (>= 2)

Maintainer

Jakob Raymaekers

Last Published

October 25th, 2023

Functions in cellWise (2.5.3)

The clothes dataset

The brands dataset

The mortality dataset

The glass dataset

data_personality_traits

The personality traits data

The philips dataset

Dog walker dataset

Estimate location and scatter of data with cellwise weights

Draw plots based on the cellwise minimum covariance determinant estimator cellMCD

Robustly fit the Box-Cox or Yeo-Johnson transformation

Generates correlation matrices

Estimate robust location and scale

Plot the outlier map.

Generates artificial datasets with outliers

Estimates location and scatter on incomplete data with case weights

Unpacks cellwise weighted data

Classical Principal Components by truncated SVD.

transfo_newdata

Transform variables based on the output of transfo.

transfo_transformback

Backtransform variables based on the output of transfo.

Iterative Classical PCA

cellWise minimum covariance determinant estimator

MacroPCApredict

MacroPCApredict

Detection-Imputation algorithm

Detect Deviating Cells

cellHandler algorithm

Clean the dataset