This package provides functions for working with random matrices. It also provides various convenience functions for examining data within matrices as well as some optimized functions for reading matrices in various formats.
Random matrix ensembles can be created using this package. It's also possible to fit the Marcenko-Pastur distribution to Wishart matrices, enabling you to isolate the noise portion of the eigenvalue spectrum.
Brian Lee Yung Rowe <[email protected]>
The Distribution Functions of Random Matrix Theory, Craig A. Tracy, UC Davis http://www.math.ucsc.edu/research/rmtg.html
Introduction to the Random Matrix Theory: Gaussian Unitary Ensemble and Beyond http://arxiv.org/abs/math-ph/0412017v2
Tyler's M-Estimator, Random Matrix Theory, and Generalized Elliptical Distributions with Applications to Finance http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1287683
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# Generate a random ensemble m <- rmatrix(WishartModel(100,400)) # Select sub-matrices library(datasets) select(swiss, "Rive") select(swiss, col.pat='^E') select(swiss, "Rive", '^E') <- -1 dimnames <- list( c(rownames(swiss), 'Zermat', 'Zurich', 'Geneva'), c(colnames(swiss), 'Age','Hair.Color') ) my.swiss <- expand(swiss, dimnames)
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