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Visualization and Imputation of Missing Values
New tools for the visualization of missing and/or imputed values are introduced, 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. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
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.
Extensions of Package 'distr'
Extends package 'distr' by functionals, distances, and conditional 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.
Clustering of Weighted Data
Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions and plotting clusters of state sequences.