fastcluster: Fast Hierarchical Clustering Routines for R and 'Python'
This is a two-in-one package which provides interfaces to
both R and 'Python'. It implements fast hierarchical, agglomerative
clustering routines. Part of the functionality is designed as drop-in
replacement for existing routines: linkage() in the 'SciPy' package
'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
'flashClust' package. It provides the same functionality with the
benefit of a much faster implementation. Moreover, there are
memory-saving routines for clustering of vector data, which go beyond
what the existing packages provide. For information on how to install
the 'Python' files, see the file INSTALL in the source distribution.
Based on the present package, Christoph Dalitz also wrote a pure 'C++'
interface to 'fastcluster':
<https://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/>.
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: |
bahc, cummeRbund, ICGE, PropClust, WGCNA |
| Reverse imports: |
adductomicsR, aRchi, CEMiTool, chromatographR, ClustAssess, cogena, cstab, disto, geva, heatmap3, hierGWAS, HierPortfolios, hypervolume, iheatmapr, ILoReg, infercnv, Linnorm, maotai, MetaCyto, MetProc, MLGL, mousetrap, MRPC, msPurity, NPflow, pagoda2, propr, RAMClustR, scGPS, SpatialVx, vanddraabe, visxhclust, VoxR |
| Reverse suggests: |
BrainSABER, ComplexHeatmap, familiar, FCPS, hyperSpec, IncDTW, linkcomm, scde |
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