Imports: DMwR, randomForest, caret, clustertend, stats, e1071, methods,

Description: Preprocessing is often the most time-consuming phase in data analysis
and preprocessing transformations interdependent in unexpected
provides an S4 framework for creating and evaluating preprocessing combinations
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.
Suggests: kernlab, rpart, testthat, knitr, rmarkdown, ggplot2, lattice,

Title: Lightweight Data Quality Simulation for Classification
Authors@R: person("Markus", "Vattulainen", email = "markus.vattulainen@gmail.com", role = c("aut", "cre"))
Description: Data quality simulation can be used to check the robustness of data
analysis findings and learn about the impact of data quality contaminations on
classification. This package helps to add contaminations (noise, missing values,
outliers, low variance, irrelevant features, class swap (inconsistency), class
imbalance and decrease in data volume) to data and then evaluate the simulated
data sets for classification accuracy. As a lightweight solution simulation runs
can be set up with no or minimal up-front effort.
Imports: DMwR, reshape2, ggplot2, methods, stats, caret, doParallel,
Suggests: gbm, testthat, preprocomb, preproviz, knitr, rmarkdown

Suggests: gbm, preprocomb, preproviz, knitr, rmarkdown

Title: Tools for Visualization of Interdependent Data Quality Issues
Description: 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
Imports: caret, DMwR, randomForest, ClustOfVar, reshape2, ggplot2,
ggdendro, gridExtra, methods, utils, stats