Analyzing Data with Cellwise Outliers
Estimates location and scatter on incomplete data with case weights
Wrap the data.
cellHandler algorithm
Draw a cellmap
cellWise minimum covariance determinant estimator
Clean the dataset
Estimate location and scatter of data with cellwise weights
Detect Deviating Cells
DDCpredict
Detection-Imputation algorithm
Estimate robust location and scale
Generates correlation matrices
Generates artificial datasets with outliers
Iterative Classical PCA
MacroPCA
MacroPCApredict
Plot the outlier map.
Draw plots based on the cellwise minimum covariance determinant estima...
Robustly fit the Box-Cox or Yeo-Johnson transformation
Transform variables based on the output of transfo.
Backtransform variables based on the output of transfo.
Classical Principal Components by truncated SVD.
Unpacks cellwise weighted data
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) <arXiv:2207.13493> (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".