R: Analyzing Data with Cellwise Outliers

# Analyzing Data with Cellwise Outliers

## Documentation for package ‘cellWise’ version 2.5.7

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

cellHandler | cellHandler algorithm
cellMap | Draw a cellmap
cellMCD | cellWise minimum covariance determinant estimator
checkDataSet | Clean the dataset
cwLocScat | Estimate location and scatter of data with cellwise weights
data_brands | The brands dataset
data_clothes | The clothes dataset
data_dogWalker | Dog walker dataset
data_dposs | DPOSS dataset
data_glass | The glass dataset
data_mortality | The mortality dataset
data_personality_traits | The personality traits data
data_philips | The philips dataset
data_VOC | VOC dataset
DDC | Detect Deviating Cells
DDCpredict | DDCpredict
DI | Detection-Imputation algorithm
estLocScale | Estimate robust location and scale
generateCorMat | Generates correlation matrices
generateData | Generates artificial datasets with outliers
ICPCA | Iterative Classical PCA
MacroPCA | MacroPCA
MacroPCApredict | MacroPCApredict
outlierMap | Plot the outlier map.
plot_cellMCD | Draw plots based on the cellwise minimum covariance determinant estimator cellMCD
transfo | Robustly fit the Box-Cox or Yeo-Johnson transformation
transfo_newdata | Transform variables based on the output of 'transfo'.
transfo_transformback | Backtransform variables based on the output of 'transfo'.
truncPC | Classical Principal Components by truncated SVD.
unpack | Unpacks cellwise weighted data
weightedEM | Estimates location and scatter on incomplete data with case weights
wrap | Wrap the data.