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"Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.
Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
Implementation of Random Variables
Implements random variables by means of S4 classes and methods.
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'.
Object Oriented Implementation of Distributions
S4-classes and methods for distributions.
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.
Object Oriented Implementation of Probability Models
Implements S4 classes for probability models based on packages 'distr' and 'distrEx'.
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
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)