Package: RMThreshold 1.1

RMThreshold: Signal-Noise Separation in Random Matrices by using Eigenvalue Spectrum Analysis

An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.

Authors:Uwe Menzel

RMThreshold_1.1.tar.gz
RMThreshold_1.1.zip(r-4.7)RMThreshold_1.1.zip(r-4.6)RMThreshold_1.1.zip(r-4.5)
RMThreshold_1.1.tgz(r-4.6-any)RMThreshold_1.1.tgz(r-4.5-any)
RMThreshold_1.1.tar.gz(r-4.7-any)RMThreshold_1.1.tar.gz(r-4.6-any)
RMThreshold_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RMThreshold/json (API)

# Install 'RMThreshold' in R:

install.packages('RMThreshold', repos = c('https://uwemenzel.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.26 score 4 stars 23 scripts 243 downloads 2 mentions 29 exports 3 dependencies

Last updated from:f365567f27. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	102
source / vignettes	OK	176
linux-release-x86_64	OK	100
macos-release-arm64	OK	89
macos-oldrel-arm64	OK	115
windows-devel	OK	102
windows-release	OK	69
windows-oldrel	OK	73
wasm-release	OK	103

Exports:add.Gaussian.noise create.rand.mat kb.distance kld rm.connections rm.denoise.mat rm.discard.zeros rm.distance.plot rm.ev.density rm.ev.unfold rm.exp.distrib rm.get.distance rm.get.file.extension rm.get.sparseness rm.get.threshold rm.likelihood.plot rm.matrix.validation rm.reorder.ev rm.show.plots rm.show.test rm.spacing.distribution rm.spacing.scatter rm.sse rm.sse.plot rm.trapez.int rm.unfold.gauss rm.unfold.spline wigner.semi.circle wigner.surmise

Dependencies:lattice Matrix png

Help page	Topics
Signal-Noise Separation in Correlation Matrices by using Eigenvalue Spectrum Analysis	RMThreshold-package RMThreshold
Add Gaussian noise to a matrix	add.Gaussian.noise
Create a real-valued, symmetric random matrix	create.rand.mat
Create ordered list of largest matrix elements	rm.connections
Remove noise from a random matrix by applying a threshold	rm.denoise.mat
Discard rows and columns from a matrix that exclusively contain zero-valued off-diagonal matrix elements	rm.discard.zeros
Create a density plot and a histogram of the eigenvalue distribution	rm.ev.density
Estimate an objective threshold for signal-noise separation in random matrices	rm.get.threshold
Validate input matrix prior to threshold computation	rm.matrix.validation
Display a sequence of plots on screen	rm.show.plots
Plot the empirical distribution of the eigenvalue spacings	rm.spacing.distribution
Internal functions for the RMThreshold package	kb.distance kld rm.distance.plot rm.ev.unfold rm.exp.distrib rm.get.distance rm.get.file.extension rm.get.sparseness rm.likelihood.plot rm.reorder.ev rm.show.test rm.spacing.scatter rm.sse rm.sse.plot rm.trapez.int rm.unfold.gauss rm.unfold.spline wigner.semi.circle wigner.surmise

Package: RMThreshold 1.1

RMThreshold: Signal-Noise Separation in Random Matrices by using Eigenvalue Spectrum Analysis

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)