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.

Embed 'SWI'-'Prolog'

Interface to 'SWI'-'Prolog', < https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package 'rolog' instead. The purpose of this package is to provide the 'Prolog' runtime on systems that do not have a software installation of 'SWI'-'Prolog'.

Object Oriented Implementation of Probability Models

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

A Game for Human vs. Human or Human vs. AI

A game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin.

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.

Optimally Robust Estimation

R infrastructure for optimally robust estimation in general smoothly parameterized models using S4 classes and methods as described Kohl, M., Ruckdeschel, P., and Rieder, H. (2010), , and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), .

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.

Interactive 'tourr' Using 'python'

Extends the functionality of the 'tourr' package by an interactive graphical user interface. The interactivity allows users to effortlessly refine their 'tourr' results by manual intervention, which allows for integration of expert knowledge and aids the interpretation of results. For more information on 'tourr' see Wickham et. al (2011) or < https://github.com/ggobi/tourr>.

Descriptive Statistics

Computation of standardized interquartile range (IQR), Huber-type skipped mean (Hampel (1985), ), robust coefficient of variation (CV) (Arachchige et al. (2019), ), robust signal to noise ratio (SNR), z-score, standardized mean difference (SMD), as well as functions that support graphical visualization such as boxplots based on quartiles (not hinges), negative logarithms and generalized logarithms for 'ggplot2' (Wickham (2016), ISBN:978-3-319-24277-4).