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Performs cluster analysis using an ensemble clustering framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>. Results from a diverse set of algorithms are pooled together using methods such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to compare cluster assignments across algorithms using internal and external indices, visualizations such as heatmaps, and significance testing for the existence of clusters.

copied from cf-post-staging / r-dicer
Type Size Name Uploaded Downloads Labels
conda 1.3 MB | win-64/r-dicer-1.2.2-r41ha856d6a_0.tar.bz2  2 years and 9 months ago 526 main
conda 1.3 MB | osx-64/r-dicer-1.2.2-r41h49197e3_0.tar.bz2  2 years and 9 months ago 80 main
conda 1.3 MB | osx-64/r-dicer-1.2.2-r42h49197e3_0.tar.bz2  2 years and 9 months ago 78 main
conda 1.3 MB | linux-64/r-dicer-1.2.2-r42h7525677_0.tar.bz2  2 years and 9 months ago 2139 main
conda 1.3 MB | linux-64/r-dicer-1.2.2-r41h7525677_0.tar.bz2  2 years and 9 months ago 2095 main

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