Visualization and Imputation of Missing Values

Provides methods for imputation and visualization of missing values. It includes graphical tools to explore the amount, structure and patterns of missing and/or imputed values, supporting exploratory data analysis and helping to investigate potential missingness mechanisms (details in Alfons, Templ and Filzmoser, . The quality of imputations can be assessed visually using a wide range of univariate, bivariate and multivariate plots. The package further provides several imputation methods, including efficient implementations of k-nearest neighbour and hot-deck imputation (Kowarik and Templ 2013, , iterative robust model-based multiple imputation (Templ 2011, ; Templ 2023, ), and machine learning–based approaches such as robust GAM-based multiple imputation (Templ 2024, ) as well as gradient boosting (XGBoost) and transformer-based methods (Niederhametner et al., ). General background and practical guidance on imputation are provided in the Springer book by Templ (2023) .

R Unit Test Framework

R functions implementing a standard Unit Testing framework, with additional code inspection and report generation tools.

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.

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.

"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".

Extensions of Package 'distr'

Extends package 'distr' by functionals, distances, and conditional distributions.

Compositional Data Analysis

Methods for analysis of compositional data including robust methods (), imputation of missing values (), methods to replace rounded zeros (, , ), count zeros (), methods to deal with essential 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, functional data analysis () and p-splines (), contingency () and compositional tables (, , ) 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.

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, sequence analysis typology validation using parametric bootstraps and plotting (fuzzy) clusters of state sequences. It further provides a fuzzy and crisp CLARA algorithm to cluster large database with sequence analysis, and a methodological framework for Robustness Assessment of Regressions using Cluster Analysis Typologies (RARCAT).

Statistical Disclosure Control Methods for Anonymization of Data and Risk Estimation

Data from statistical agencies and other institutions are mostly confidential. This package, introduced in Templ, Kowarik and Meindl (2017) , can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files. The theoretical basis for the methods implemented can be found in Templ (2017) . Various risk estimation and anonymization methods are included. Note that the package includes a graphical user interface published in Meindl and Templ (2019) that allows to use various methods of this package.