Title:  'Rcpp' Integration for the 'Eigen' Templated Linear Algebra Library 

Description:  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 level3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library. 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'. 
Authors:  Doug Bates [aut] , Dirk Eddelbuettel [aut, cre] , Romain Francois [aut] , Yixuan Qiu [aut] , Authors of Eigen [cph] (Authorship and copyright in included Eigen library as detailed in inst/COPYRIGHTS) 
Maintainer:  Dirk Eddelbuettel <[email protected]> 
License:  GPL (>= 2)  file LICENSE 
Version:  0.3.4.0.2 
Built:  20241029 05:17:01 UTC 
Source:  https://github.com/rcppcore/rcppeigen 
The package eases the use of the Eigen C++ template library for linear algebra with Rcpp
This package contains the header files for the Eigen C++ template library. The typical usage is to install this package and list it in the LinkingTo: line in the ‘DESCRIPTION’ file of other packages. The C++ source code and the R source code in this package are for illustration only.
As described at the Eigen project's home page, http://eigen.tuxfamily.org/index.php?title=Main_Page, Eigen is a versatile, fast, reliable and elegant collection of C++ classes for linear algebra.
Douglas Bates and Dirk Eddelbuettel (2013). Fast and Elegant Numerical Linear Algebra Using the RcppEigen Package. Journal of Statistical Software, 52(5), 124. URL http://www.jstatsoft.org/v52/i05/.
fastLm
estimates the linear model using one of several methods
implemented using the Eigen
linear algebra library.
fastLmPure(X, y, method = 0L) fastLm(X, ...) ## Default S3 method: fastLm(X, y, method = 0L, ...) ## S3 method for class 'formula' fastLm(formula, data = list(), method = 0L, ...)
fastLmPure(X, y, method = 0L) fastLm(X, ...) ## Default S3 method: fastLm(X, y, method = 0L, ...) ## S3 method for class 'formula' fastLm(formula, data = list(), method = 0L, ...)
y 
the response vector 
X 
a model matrix 
formula 
an object of class 
data 
an optional data frame, list or environment (or object
coercible by 
method 
an integer scalar with value 0 for the columnpivoted QR
decomposition, 1 for the unpivoted QR decomposition, 2 for the LLT
Cholesky, 3 for the LDLT Cholesky, 4 for the Jacobi singular value
decomposition (SVD) and 5 for a method based on the
eigenvalueeigenvector decomposition of

... 
not used 
Linear models should be estimated using the lm
function. In
some cases, lm.fit
may be appropriate.
The fastLmPure
function provides a reference use case of the Eigen
C++ template library via the wrapper functions in the RcppEigen package.
The fastLm
function provides a more standard implementation of
a linear model fit, offering both a default and a formula interface as
well as print
, summary
and predict
methods.
Internally the fastLm
function, by default, uses a QR
decomposition with column pivots, which is a rankrevealing
decomposition, so that it can handle rankdeficient cases
effectively. Other methods for determining least squares solutions
are available according to the value of the method
argument.
An example of the type of situation requiring extra care in checking for rank deficiency is a twoway layout with missing cells (see the examples section). These cases require a special pivoting scheme of “pivot only on (apparent) rank deficiency” which is not part of conventional linear algebra software.
fastLmPure
returns a list with several components:
coefficients 
a vector of coefficients 
se 
a vector of the standard errors of the coefficient estimates 
rank 
a scalar denoting the computed rank of the model matrix 
df.residual 
a scalar denoting the degrees of freedom in the model 
residuals 
the vector of residuals 
s 
a numeric scalar  the root mean square for residuals 
fitted.values 
the vector of fitted value 
fastLm
returns a richer object which also includes the
call argument similar to the lm
or
rlm
functions..
Eigen is described at http://eigen.tuxfamily.org/index.php?title=Main_Page. RcppEigen is written by Douglas Bates, Dirk Eddelbuettel and Romain Francois.
Douglas Bates and Dirk Eddelbuettel (2013). Fast and Elegant Numerical Linear Algebra Using the RcppEigen Package. Journal of Statistical Software, 52(5), 124. URL http://www.jstatsoft.org/v52/i05/.
data(trees, package="datasets") mm < cbind(1, log(trees$Girth)) # model matrix y < log(trees$Volume) # response ## barebones direct interface flm < fastLmPure(mm, y) print(flm) ## standard R interface for formula or data returning object of class fastLm flmmod < fastLm( log(Volume) ~ log(Girth), data=trees) summary(flmmod) ## case where nonrankrevealing methods break down dd < data.frame(f1 = gl(4, 6, labels = LETTERS[1:4]), f2 = gl(3, 2, labels = letters[1:3]))[(7:8), ] xtabs(~ f2 + f1, dd) # one missing cell mm < model.matrix(~ f1 * f2, dd) kappa(mm) # large, indicating rank deficiency set.seed(1) dd$y < mm %*% seq_len(ncol(mm)) + rnorm(nrow(mm), sd = 0.1) summary(lm(y ~ f1 * f2, dd)) # detects rank deficiency try(summary(fastLm(y ~ f1 * f2, dd))) # also detects rank deficiency
data(trees, package="datasets") mm < cbind(1, log(trees$Girth)) # model matrix y < log(trees$Volume) # response ## barebones direct interface flm < fastLmPure(mm, y) print(flm) ## standard R interface for formula or data returning object of class fastLm flmmod < fastLm( log(Volume) ~ log(Girth), data=trees) summary(flmmod) ## case where nonrankrevealing methods break down dd < data.frame(f1 = gl(4, 6, labels = LETTERS[1:4]), f2 = gl(3, 2, labels = letters[1:3]))[(7:8), ] xtabs(~ f2 + f1, dd) # one missing cell mm < model.matrix(~ f1 * f2, dd) kappa(mm) # large, indicating rank deficiency set.seed(1) dd$y < mm %*% seq_len(ncol(mm)) + rnorm(nrow(mm), sd = 0.1) summary(lm(y ~ f1 * f2, dd)) # detects rank deficiency try(summary(fastLm(y ~ f1 * f2, dd))) # also detects rank deficiency
RcppEigen.package.skeleton
automates the creation of
a new source package that intends to use features of RcppEigen.
It is based on the package.skeleton function which it executes first.
RcppEigen.package.skeleton(name = "anRpackage", list = character(), environment = .GlobalEnv, path = ".", force = FALSE, code_files = character(), example_code = TRUE)
RcppEigen.package.skeleton(name = "anRpackage", list = character(), environment = .GlobalEnv, path = ".", force = FALSE, code_files = character(), example_code = TRUE)
name 
See package.skeleton 
list 
See package.skeleton 
environment 
See package.skeleton 
path 
See package.skeleton 
force 
See package.skeleton 
code_files 
See package.skeleton 
example_code 
If TRUE, example C++ code using RcppEigen is added to the package 
In addition to package.skeleton :
The ‘DESCRIPTION’ file gains a Depends line requesting that the package depends on Rcpp and RcppEigen and a LinkingTo line so that the package finds Rcpp and RcppEigen header files.
The ‘NAMESPACE’ gains a useDynLib
directive.
The ‘src’ directory is created if it does not exists and a ‘Makevars’ file is added setting the environment variable ‘PKG_LIBS’ to accomodate the necessary flags to link with the Rcpp library.
If the example_code
argument is set to TRUE
,
example files ‘rcppeigen_hello_world.h’ and ‘rcppeigen_hello_world.cpp’
are also created in the ‘src’. An R file ‘rcppeigen_hello_world.R’ is
expanded in the ‘R’ directory, the rcppeigen_hello_world
function
defined in this files makes use of the C++ function ‘rcppeigen_hello_world’
defined in the C++ file. These files are given as an example and should
eventually by removed from the generated package.
Nothing, used for its side effects
Read the Writing R Extensions manual for more details.
Once you have created a source package you need to install it:
see the R Installation and Administration manual,
INSTALL
and install.packages
.
## Not run: RcppEigen.package.skeleton("foobar") ## End(Not run)
## Not run: RcppEigen.package.skeleton("foobar") ## End(Not run)