cyclesampler-package: cyclesampler: Randomization Algorithms for Large Sparse...
In edahelsinki/cyclesampler: Randomization Algorithms for Large Sparse Matrices
Description
A property-preserving Markov Chain Monte Carlo method for
generating surrogate networks in which (i) edge weights
are constrained to an interval and vertex weights are
preserved exactly, and (ii) edge and vertex weights are
both constrained to intervals.
Author(s)
Maintainer: Kai Puolamäki kai.puolamaki@iki.fi
Other contributors:
University of Helsinki [copyright holder]
edahelsinki/cyclesampler documentation built on June 9, 2019, 10:51 a.m.
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Description
A property-preserving Markov Chain Monte Carlo method for generating surrogate networks in which (i) edge weights are constrained to an interval and vertex weights are preserved exactly, and (ii) edge and vertex weights are both constrained to intervals.
Author(s)
Maintainer: Kai Puolamäki kai.puolamaki@iki.fi
Other contributors:
University of Helsinki [copyright holder]
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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