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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.
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.
R Unit Test Framework
R functions implementing a standard Unit Testing framework, with additional code inspection and report generation tools.
Implementation of Random Variables
Implements random variables by means of S4 classes and methods.
Compositional Data Analysis
Methods for analysis of compositional data including robust methods, imputation, methods to replace rounded zeros, (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.
"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.
Various Coefficients of Interrater Reliability and Agreement
Coefficients of Interrater Reliability and Agreement for quantitative, ordinal and nominal data: ICC, Finn-Coefficient, Robinson's A, Kendall's W, Cohen's Kappa, ...
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.