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Robust Estimation for Compositional Data
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.
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.
Statistical Disclosure Control Methods for Anonymization of Microdata 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.
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.
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.
Sparklines and Graphical Tables for TeX and HTML
Create sparklines and graphical tables for documents and websites.
Visualization and Imputation of Missing Values - Graphical User Interface
A graphical user interface for the methods implemented in the package VIM. It allows an easy handling of the implemented plot and imputation methods.
Graphical User Interface for Package sdcMicro
DEPRECATED: A new version of the graphical user interface is available directly in the package sdcMicro via function sdcApp(). A point and click graphical user interface based on top of the 'sdcMicro' package to create anonymized data set. The graphical user interface provides full reproducibility of any result via the script menu in the GUI.
Estimating and Mapping Disaggregated Indicators
Functions that support estimating, assessing and mapping regional
disaggregated indicators. So far, estimation methods comprise direct estimation
and the model-based approach Empirical Best Prediction (see "Small area
estimation of poverty indicators" by Molina and Rao (2010)
Tools for Descriptive Statistics
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.