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VIM - 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, <doi:10.1007/s11634-011-0102-y>). 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, <doi:10.18637/jss.v074.i07>), iterative robust model-based multiple imputation (Templ 2011, <doi:10.1016/j.csda.2011.04.012>; Templ 2023, <doi:10.3390/math11122729>), and machine learning–based approaches such as robust GAM-based multiple imputation (Templ 2024, <doi:10.1007/s11222-024-10429-1>) as well as random forest and gradient boosting (XGBoost) imputation (Niederhametner et al., <doi:10.1177/18747655251339401>). General background and practical guidance on imputation are provided in the Springer book by Templ (2023) <doi:10.1007/978-3-031-30073-8>.

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hotdeckimputation-methodsmodel-predictionsvisualizationcppopenmp

15.51 score 93 stars 23 dependents 3.8k scripts 16k downloads

sdcMicro - 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) <doi:10.18637/jss.v067.i04>, 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) <doi:10.1007/978-3-319-50272-4>. Various risk estimation and anonymization methods are included. Note that the package includes a graphical user interface published in Meindl and Templ (2019) <doi:10.3390/a12090191> that allows to use various methods of this package.

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cpp

10.37 score 93 stars 288 scripts 1.3k downloads

robCompositions - Compositional Data Analysis

Methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (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 (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) 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.

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cpp

9.64 score 12 stars 3 dependents 229 scripts 1.2k downloads

simPop - 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) <doi:10.18637/jss.v079.i10>) and Templ (2017) <doi:10.1007/978-3-319-50272-4>. The package was developed with support of the International Household Survey Network, DFID Trust Fund TF011722 and funds from the World bank.

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cpp

6.82 score 34 stars 129 scripts 676 downloads

pep725 - 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) <doi:10.1007/s00484-018-1512-8>; Templ et al. (2026) <doi:10.1111/nph.70869>) 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'.

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5.82 score 3 stars 11 scripts 368 downloads

riskutility - 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) <doi:10.1007/978-3-319-99771-1_9> and related literature. Designed for integration with 'simPop' S4 classes.

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5.45 score 2 stars 6 scripts 482 downloads

heaping - 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.

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3.70 score 8 scripts 263 downloads

deepImp - Imputation with Deep Learning Methods

Imputation of mixed-type and compositional data with neural networks. The architecture (number and size of hidden layers, dropout, activation, optimiser) is user-configurable. See Templ (2021) <doi:10.1007/978-3-030-71175-7>.

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2.70 score 5 scripts 229 downloads