Computes standard error and confidence interval of various descriptive statistics under 
    various designs and sampling schemes. The main function, superbPlot(), return a plot. superbData() 
    returns a dataframe with the statistic and its precision interval so that other plotting package
    can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> 
    or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005)
    <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012)
    <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>,
    Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding 
    <doi:10.20982/tqmp.15.1.p001> for specific references.

Biclusters are submatrices in the data matrix which
        satisfy certain conditions of homogeneity. Package contains
        functions for generating robust biclusters with respect to the
        initialization parameters for a given bicluster solution
        contained in a bicluster set in data, the procedure is also
        known as ensemble biclustering. The set of biclusters is
        evaluated based on the similarity of its elements (the
        overlap), and afterwards the hierarchical tree is constructed
        to obtain cut-off points for the classes of robust biclusters.
        The result is a number of robust (or super) biclusters with
        none or low overlap.

Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells. 'SuperCell' uses 'velocyto.R' <doi:10.1038/s41586-018-0414-6> <https://github.com/velocyto-team/velocyto.R> for RNA velocity. We also recommend installing 'scater' Bioconductor package <doi:10.18129/B9.bioc.scater> <https://bioconductor.org/packages/release/bioc/html/scater.html>.

Creates superpixels based on input spatial data. 
  This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters).
  It is based on the SLIC algorithm (Achanta et al. (2012) <doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary dissimilarity measures.