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.

Implementation of Random Variables

Implements random variables by means of S4 classes and methods.

Object Oriented Implementation of Distributions

S4-classes and methods for distributions.

Data Visualization Tools for Statistical Analysis Results

Unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using 'ggplot2'.

Relative Importance of Regressors in Linear Models

Provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.

Robust Asymptotic Statistics

Base S4-classes and functions for robust asymptotic statistics.

Analysis of Music and Speech

Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read mp3, read midi, perform steps of a transcription, ... Also contains functions ported from the 'rastamat' 'Matlab' package.

Object Oriented Implementation of Probability Models

Implements S4 classes for probability models based on packages 'distr' and 'distrEx'.

Model-Based Boosting

Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in , a hands-on tutorial is available from . The package allows user-specified loss functions and base-learners.

Statistical Inference of Vine Copulas

Provides tools for the statistical analysis of regular vine copula models, see Aas et al. (2009) and Dissman et al. (2013) . The package includes tools for parameter estimation, model selection, simulation, goodness-of-fit tests, and visualization. Tools for estimation, selection and exploratory data analysis of bivariate copula models are also provided.