Fast Gaussian Process Computation Using Vecchia's Approximation

Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) < http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) . Model fitting employs a Fisher scoring algorithm described in Guinness (2019) .

Inferential Statistics

Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), ), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015), ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999), ) t-test; furthermore, computation of intersection-union z-test as well as multiple imputation Wilcoxon tests. Graphical visualizations: volcano plot, Bland-Altman plots (Bland and Altman (1986), ; Shieh (2018), ), mean difference plot (Boehning et al. (2008), ), plot of test statistic for permutation and bootstrap tests as well as objects of class htest.

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.

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.

Object Oriented Implementation of Probability Models

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

Estimators of Prediction Accuracy for Time-to-Event Data

Provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data.

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), .

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.