tag:crantastic.org,2005:/authors/1188Latest activity for Uwe Menzel to2016-06-23T18:21:21Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/523132016-06-23T18:21:21Z2016-06-23T18:21:21ZRMThreshold was upgraded to version 1.1<a href="/packages/RMThreshold">RMThreshold</a> was <span class="action">upgraded</span> to version <a href="/packages/RMThreshold/versions/50513">1.1</a><br /><h3>Package description:</h3><p>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.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/522172016-06-21T09:21:09Z2016-06-21T09:21:09ZRMThreshold was released<a href="/packages/RMThreshold">RMThreshold</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>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.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/289162013-01-29T09:30:22Z2013-01-29T09:30:22ZEMT was upgraded to version 1.1<a href="/packages/EMT">EMT</a> was <span class="action">upgraded</span> to version <a href="/packages/EMT/versions/24699">1.1</a><br /><h3>Package description:</h3><p>The package provides functions to carry out a Goodness-of-fit test for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. A p-value can be calculated using different distance measures between observed and expected frequencies. A Monte Carlo method is provided to make the package capable of solving high-dimensional problems.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/163222011-09-27T10:50:10Z2011-09-27T10:50:10ZCCP was upgraded to version 1.1<a href="/packages/CCP">CCP</a> was <span class="action">upgraded</span> to version <a href="/packages/CCP/versions/14532">1.1</a><br /><h3>Package description:</h3><p>Significance tests for canonical correlation analysis, including asymptotic tests and a Monte Carlo method</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/54752010-04-24T19:31:15Z2010-04-24T19:31:15ZEMT was released<a href="/packages/EMT">EMT</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>The package provides functions to carry out a Goodness-of-fit test for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. A p-value can be calculated using different distance measures between observed and expected frequencies. A Monte Carlo method is provided to make the package capable of solving high-dimensional problems.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/33492009-12-21T13:11:02Z2009-12-21T13:11:02ZCCP was released<a href="/packages/CCP">CCP</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Significance tests for canonical correlation analysis, including asymptotic tests and a Monte Carlo method</p>crantastic.org