preproviz: Tools for Visualization of Interdependent Data Quality Issues

Data quality issues such as missing values and outliers are often interdependent, which makes preprocessing both time-consuming and leads to suboptimal performance in knowledge discovery tasks. This package supports preprocessing decision making by visualizing interdependent data quality issues through means of feature construction. The user can define his own application domain specific constructed features that express the quality of a data point such as number of missing values in the point or use nine default features. The outcome can be explored with plot methods and the feature constructed data acquired with get methods.

Version: 0.2.0
Depends: R (≥ 3.2.2)
Imports: caret, DMwR, randomForest, ClustOfVar, reshape2, ggplot2, ggdendro, gridExtra, methods, utils, stats
Suggests: testthat, rmarkdown, knitr, preprocomb
Published: 2016-07-09
Author: Markus Vattulainen [aut, cre]
Maintainer: Markus Vattulainen <markus.vattulainen at gmail.com>
BugReports: https://github.com/mvattulainen/preproviz/issues
License: GPL-2
URL: https://github.com/mvattulainen/preproviz
NeedsCompilation: no
CRAN checks: preproviz results

Downloads:

Reference manual: preproviz.pdf
Vignettes: Preproviz
Package source: preproviz_0.2.0.tar.gz
Windows binaries: r-devel: preproviz_0.2.0.zip, r-release: preproviz_0.2.0.zip, r-oldrel: preproviz_0.2.0.zip
OS X Mavericks binaries: r-release: preproviz_0.2.0.tgz, r-oldrel: preproviz_0.2.0.tgz
Old sources: preproviz archive

Reverse dependencies:

Reverse suggests: preprocomb, preprosim

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