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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  3 years and 7 months ago 551 main
conda 1.3 MB | osx-64/r-dicer-1.2.2-r41h49197e3_0.tar.bz2  3 years and 7 months ago 98 main
conda 1.3 MB | osx-64/r-dicer-1.2.2-r42h49197e3_0.tar.bz2  3 years and 7 months ago 96 main
conda 1.3 MB | linux-64/r-dicer-1.2.2-r42h7525677_0.tar.bz2  3 years and 7 months ago 2750 main
conda 1.3 MB | linux-64/r-dicer-1.2.2-r41h7525677_0.tar.bz2  3 years and 7 months ago 2697 main

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