spam (version 2.1-1)

. SPAM .: SPArse Matrix Package

Description

spam is a collection of functions for sparse matrix algebra.

Arguments

Gereral overview

What is spam and what is it not:

While 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. We deliberately avoided aliases according to analogue helps from the base package.

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 spam are closely linked.

References

Reinhard Furrer, Stephan R. Sain (2010). "spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields.", Journal of Statistical Software, 36(10), 1-25, http://www.jstatsoft.org/v36/i10/.

See Also

See spam.class for a detailed class description, spam and spam.ops for creation, coercion and algebraic operations.

Examples

Run this code
# NOT RUN {
## Citations:
citation('spam')
citation('spam',auto=TRUE)


## History of changes
# }
# NOT RUN {
file.show(system.file("NEWS", package = "spam"))
# }

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