Package: hierBipartite
Type: Package
Title: Bipartite Graph-Based Hierarchical Clustering
Version: 0.0.2
Authors@R: c(person("Calvin", "Chi", email = "calvin.chi@berkeley.edu", role = c("aut", "cre", "cph")),
           person("Woojoo", "Lee", role = c("ctb")),
           person("Donghwan", "Lee", role = c("ctb")),
           person("Youngjo", "Lee", role = c("ctb")),
           person("Yudi", "Pawitan", role = c("ctb")))
Maintainer: Calvin Chi <calvin.chi@berkeley.edu>
Description: Bipartite graph-based hierarchical clustering performs
        hierarchical clustering of groups of samples based on
        association patterns between two sets of variables. It is
        developed for pharmacogenomic datasets and datasets sharing the
        same data structure. In the context of pharmacogenomic
        datasets, the samples are cell lines, and the two sets of
        variables are typically expression levels and drug sensitivity
        values. For this method, sparse canonical correlation analysis
        from Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011)
        <doi:10.2202/1544-6115.1638> is first applied to extract
        association patterns for each group of samples. Then, a nuclear
        norm-based dissimilarity measure is used to construct a
        dissimilarity matrix between groups based on the extracted
        associations. Finally, hierarchical clustering is applied.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: parallel, magrittr, irlba
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
Repository: https://calvintchi.r-universe.dev
Date/Publication: 2021-02-20 16:14:25 UTC
RemoteUrl: https://github.com/calvintchi/hierbipartite
RemoteRef: HEAD
RemoteSha: 02d69545d0ff3998c72e4068592c1116f62eb0bb
NeedsCompilation: no
Packaged: 2026-10-09 11:58:48 UTC; root
Author: Calvin Chi [aut, cre, cph],
  Woojoo Lee [ctb],
  Donghwan Lee [ctb],
  Youngjo Lee [ctb],
  Yudi Pawitan [ctb]