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Support Functions and Datasets for Venables and Ripley's MASS
Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).
Seamless R and C++ Integration
The 'Rcpp' package provides R functions as well as C++ classes which
offer a seamless integration of R and C++. Many R data types and objects can be
mapped back and forth to C++ equivalents which facilitates both writing of new
code as well as easier integration of third-party libraries. Documentation
about 'Rcpp' is provided by several vignettes included in this package, via the
'Rcpp Gallery' site at < http://gallery.rcpp.org>, the paper by Eddelbuettel and
Sparse and Dense Matrix Classes and Methods
A rich hierarchy of matrix classes, including triangular, symmetric, and diagonal matrices, both dense and sparse and with pattern, logical and numeric entries. Numerous methods for and operations on these matrices, using 'LAPACK' and 'SuiteSparse' libraries.
Tools for Spatial Data
For curve, surface and function fitting with an emphasis
on splines, spatial data, geostatistics, and spatial statistics. The major methods
include cubic, and thin plate splines, Kriging, and compactly supported
covariance functions for large data sets. The splines and Kriging methods are
supported by functions that can determine the smoothing parameter
(nugget and sill variance) and other covariance function parameters by cross
validation and also by restricted maximum likelihood. For Kriging
there is an easy to use function that also estimates the correlation
scale (range parameter). A major feature is that any covariance function
implemented in R and following a simple format can be used for
spatial prediction. There are also many useful functions for plotting
and working with spatial data as images. This package also contains
an implementation of sparse matrix methods for large spatial data
sets and currently requires the sparse matrix (spam) package. Use
help(fields) to get started and for an overview. The fields source
code is deliberately commented and provides useful explanations of
numerical details as a companion to the manual pages. The commented
source code can be viewed by expanding the source code version
and looking in the R subdirectory. The reference for fields can be generated
by the citation function in R and has DOI
Output Analysis and Diagnostics for MCMC
Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain.
Linear and Nonlinear Mixed Effects Models
Fit and compare Gaussian linear and nonlinear mixed-effects models.
'Rcpp' Integration for the 'Eigen' Templated Linear Algebra Library
R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library (currently version 3.3.4). Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Geographic Data Analysis and Modeling
Reading, writing, manipulating, analyzing and modeling of spatial data. The package implements basic and high-level functions for raster data and for vector data operations such as intersections. See the manual and tutorials on < https://rspatial.org/> to get started.
Linear Mixed-Effects Models using 'Eigen' and S4
Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
Companion to Applied Regression
Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, 2019.