bookdown, knitr, rmarkdown, ggplot2, gridBase

Preprocessing is often the most time-consuming phase in knowledge
discovery 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 testing preprocessing combinations
for classification, clustering and outlier detection. The framework supports
user-defined and domain-specific 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.
DMwR, randomForest, caret, caretEnsemble, methods,
clustertend, e1071, stats, utils
'00Utils.R' '01DataClass.R' '02PhaseClass.R' '03BaseClass.R'
'04GridClass.R' '05PredictionControlClass.R'
'06PreProCombClass.R'

DMwR, randomForest, caret, clustertend, stats, e1071, methods,
utils, arules, zoo
'00Utils.R' '01DataClass.R' '02PhaseClass.R'
'03PreprocessorClass.R' '04GridClass.R'
'05PredictionControlClass.R' '06PreProCombClass.R'
'07AnalysisComponent.R' '08DefaultPreprocessorsAndPhases.R'

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.
DMwR, randomForest, caret, clustertend, stats, e1071, methods,
utils, arules, zoo, doParallel, foreach
kernlab, rpart, testthat, knitr, rmarkdown, ggplot2, lattice,
preproviz

Lightweight Data Quality Simulation for Classification
person("Markus", "Vattulainen", email = "markus.vattulainen@gmail.com", role = c("aut", "cre"))
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.
DMwR, reshape2, ggplot2, methods, stats, caret, doParallel,
foreach
gbm, testthat, preprocomb, preproviz, knitr, rmarkdown