Preprocessing is often the most time-consuming phase in data analysis and preprocessing transformations interdependent in unexpected ways. This package helps to make preprocessing faster and more effective. It provides an S4 framework for creating and evaluating preprocessing combinations for classification, clustering and outlier detection. The framework supports adding of user-defined preprocessors and preprocessing phases. Default preprocessors can be used for low variance removal, missing value imputation, scaling, outlier removal, noise smoothing, feature selection and class imbalance correction.

Documentation

Manual: preprocomb.pdf
Vignette: Preprocomb

Maintainer: Markus Vattulainen <markus.vattulainen at gmail.com>

Author(s): Markus Vattulainen

Install package and any missing dependencies by running this line in your R console:

install.packages("preprocomb")

Depends R (>= 2.10)
Imports DMwR, randomForest, caret, clustertend, stats, e1071, methods, utils, arules, zoo, doParallel, foreach
Suggests kernlab, rpart, testthat, knitr, rmarkdown, ggplot2, lattice, preproviz
Enhances
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preprosim, preproviz
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Package preprocomb
Materials
URL https://github.com/mvattulainen/preprocomb
Task Views
Version 0.3.0
Published 2016-06-26
License GPL-2
BugReports https://github.com/mvattulainen/preprocomb/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks preprocomb check results
Package source preprocomb_0.3.0.tar.gz