Package: ClusterSignificance
Title: The ClusterSignificance package provides tools to assess if
        class clusters in dimensionality reduced data representations
        have a separation different from permuted data
Version: 1.6.0
Author: Jason T. Serviss and Jesper R. Gadin
Maintainer: Jason T Serviss <jason.serviss@ki.se>
Description: The ClusterSignificance package provides tools 
    to assess if class clusters in dimensionality reduced 
    data representations have a separation different from 
    permuted data. The term class clusters here refers to, 
    clusters of points representing known classes in the data. 
    This is particularly useful to determine if a subset of the
    variables, e.g. genes in a specific pathway, alone can 
    separate samples into these established classes. 
    ClusterSignificance accomplishes this by, projecting all 
    points onto a one dimensional line. Cluster separations 
    are then scored and the probability of the seen separation 
    being due to chance is evaluated using a permutation method.
Depends: R (>= 3.3.0)
BugReports: https://github.com/jasonserviss/ClusterSignificance/issues
Imports: methods, pracma, princurve, scatterplot3d, RColorBrewer,
        grDevices, graphics, utils, stats
License: GPL-3
LazyData: true
Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics
VignetteBuilder: knitr
biocViews: Clustering, Classification, PrincipalComponent,
        StatisticalMethod
NeedsCompilation: no
Collate: 'ClusterSignificance-package.R' 'All-classes.R'
        'classifier-methods.R' 'initialize-methods.R' 'mlpMatrix.R'
        'pcpMatrix.R' 'permutation-methods.R' 'plot-methods.R'
        'projection-methods.R' 'show-methods.R'
RoxygenNote: 6.0.1
Packaged: 2017-10-31 01:39:57 UTC; biocbuild
Built: R 3.4.2; ; 2017-10-31 03:42:08 UTC; windows
