R Unit Test Framework

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

Machine Learning Benchmark Problems

A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.

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.

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, ...

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.

Pan-European Phenological Data Analysis

Provides a framework for quality-aware analysis of ground-based phenological data from the PEP725 Pan-European Phenology Database (Templ et al. (2018) ; Templ et al. (2026) ) and similar observation networks. Implements station-level data quality grading, outlier detection, phenological normals (climate baselines), anomaly detection, elevation and latitude gradient estimation with robust regression, spatial synchrony quantification, partial least squares (PLS) regression for identifying temperature-sensitive periods, and sequential Mann-Kendall trend analysis. Supports data import from PEP725 files, conversion of user-supplied data, and downloadable synthetic datasets for teaching without barriers of registration. All analysis outputs provide 'print', 'summary', and 'plot' methods. Interactive spatial visualization is available via 'leaflet'.

Extensions of Package 'distr'

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

Correction of Heaping on Individual Level

Provides methods for correcting heaping (digit preference) in survey data at the individual record level. Age heaping, where respondents disproportionately report ages ending in 0 or 5, is a common phenomenon that can distort demographic analyses. Unlike traditional smoothing methods that only correct aggregated statistics, this package corrects individual values by replacing a calculated proportion of heaped observations with draws from fitted truncated distributions (log-normal, normal, or uniform). Supports 5-year and 10-year heaping patterns, single heap correction, survey weights, and optional covariate-conditional (model-based) correction via quantile regression forests or linear models to preserve relationships. A multiple-imputation wrapper repeats the correction to propagate the added uncertainty into downstream inference.

Simulation of Complex Synthetic Data Information

Tools and methods to simulate populations for surveys based on auxiliary data. The tools include model-based methods, calibration and combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017) ) and Templ (2017) . The package was developed with support of the International Household Survey Network, DFID Trust Fund TF011722 and funds from the World bank.

Disclosure Risk and Data Utility Metrics for Synthetic and Anonymized Data

Provides comprehensive methods to measure disclosure risk and data utility for anonymized and synthetic data. Implements attribution-based risk metrics including Correct Attribution Probability (CAP), Targeted CAP (TCAP), Within Equivalence Class Attribution Probability (WEAP), and RAPID (Risk of Attribute Prediction-Induced Disclosure). Also provides distance-based privacy metrics such as Distance to Closest Record (DCR), Nearest Neighbor Distance Ratio (NNDR), and Identical Match Share (IMS). Utility assessment includes propensity score analysis, distribution comparisons, and various statistical tests. Methods are based on Taub et al. (2018) and related literature. Designed for integration with 'simPop' S4 classes.