spam is a collection of functions for sparse matrix
algebra.Matrix seems an overshoot of classes and SparseM
focuses mainly on regression type problem, we provide a minimal set of
sparse matrix functions fully functional for everyday spatial statistics
life. There is however some emphasize on Markov chain Monte Carlo type
calculations within the framework of (Gaussian) Markov random fields.
Emphasis is given on a comprehensive, simple, tutorial structure of the
code. The code is S4 based but (in a tutorial spirit) the functions are
in a S3 structure visible to the user (exported via NAMESPACE).
There exist many methods for sparse matrices that work identically as in
the case of ordinary matrices. All the methods are discussed in the help
and can be accessed directly via a *.spam concatenation to the
function. For example, help{chol.spam} calls the help directly,
whereas with help{chol} the user has to choose first between the
basis help and the help provided by spam.
Sparseness is used when handling large matrices. Hence, care has been
used to provide efficient and fast routines. Essentially, the functions
do not transform the sparse structure into full matrices to use standard
(available) functionality, followed by a back transform. We agree, more
operators, functions, etc. should eventually be implemented.
The packages fields and spdep use spam as a
required package.
spam.class for a detailed class description,
spam and spam.ops for creation,
coercion and algebraic operations.
demo(package='spam') lists available demos.
Related packages are fields,
Matrix and
SparseM.ontology.## History of changes
file.show(system.file("NEWS", package = "spam"))Run the code above in your browser using DataLab