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Visualization and Imputation of Missing Values
Methods for the visualization of missing and/or imputed values
are provided, which can be used for exploring the data and the structure of
the missing and/or imputed values. Depending on this structure of the missing
values, the corresponding methods may help to identify the mechanism generating
the missing values and allows to explore the data including missing values.
In addition, the quality of imputation can be visually explored using various
univariate, bivariate, multiple and multivariate plot methods (
Extensions of Package 'distr'
Extends package 'distr' by functionals, distances, and conditional distributions.
Estimation of Indicators on Social Exclusion and Poverty
Estimation of indicators on social exclusion and poverty, as well as Pareto tail modeling for empirical income distributions.
Implementation of Random Variables
Implements random variables by means of S4 classes and methods.
R Unit Test Framework
R functions implementing a standard Unit Testing framework, with additional code inspection and report generation tools.
Compositional Data Analysis
Methods for analysis of compositional data including robust
Statistical Disclosure Control Methods for Anonymization of Data and Risk Estimation
Data from statistical agencies and other institutions are mostly
confidential. This package (see also Templ, Kowarik and Meindl (2017)
"Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.
Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
Display and Analyze ROC Curves
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.