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Create and (Interactively) Modify Nested Hierarchies
Provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats. These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.
Methods for Statistical Disclosure Control in Tabular Data
Methods for statistical disclosure control in
tabular data such as primary and secondary cell suppression as described for example
in Hundepol et al. (2012)
Sparklines and Graphical Tables for TeX and HTML
Create sparklines and graphical tables for documents and websites.
Statistical Disclosure Control Methods for Anonymization of Data and Risk Estimation
Data from statistical agencies and other institutions are mostly confidential. This package can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files. In addition, various risk estimation methods are included. Note that the package includes a graphical user interface that allows to use various methods of this package.
Simulation of Synthetic Populations for Survey Data Considering Auxiliary Information
Tools and methods to simulate populations for surveys based on auxiliary data. The tools include model-based methods, calibration and combinatorial optimization algorithms. The package was developed with support of the International Household Survey Network, DFID Trust Fund TF011722 and funds from the World bank.
Voronoi Treemaps with Added Interactivity by Shiny
The d3.js framework with the plugins d3-voronoi-map, d3-voronoi-treemap and d3-weighted-voronoi
are used to generate Voronoi treemaps in R and in a shiny application.
The computation of the Voronoi treemaps are based on Nocaj and Brandes (2012)
Unit Root and Cointegration Tests for Time Series Data
Unit root and cointegration tests encountered in applied econometric analysis are implemented.
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Extreme Values in R
Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions.