preprocomb: Tools for Preprocessing Combinations

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.

Version: 0.3.0
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
Published: 2016-06-26
Author: Markus Vattulainen
Maintainer: Markus Vattulainen <markus.vattulainen at gmail.com>
BugReports: https://github.com/mvattulainen/preprocomb/issues
License: GPL-2
URL: https://github.com/mvattulainen/preprocomb
NeedsCompilation: no
CRAN checks: preprocomb results

Downloads:

Reference manual: preprocomb.pdf
Vignettes: Preprocomb
Package source: preprocomb_0.3.0.tar.gz
Windows binaries: r-devel: preprocomb_0.3.0.zip, r-release: preprocomb_0.3.0.zip, r-oldrel: preprocomb_0.3.0.zip
OS X Mavericks binaries: r-release: preprocomb_0.3.0.tgz, r-oldrel: preprocomb_0.3.0.tgz
Old sources: preprocomb archive

Reverse dependencies:

Reverse imports: metaheur
Reverse suggests: preprosim, preproviz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=preprocomb to link to this page.