Single-cell Interpretable Tensor Decomposition (scITD) employs the 
    Tucker tensor decomposition to extract multicell-type gene expression patterns 
    that vary across donors/individuals. This tool is geared for use with single-cell
    RNA-sequencing datasets consisting of many source donors. The method has a wide
    range of potential applications, including the study of inter-individual variation
    at the population-level, patient sub-grouping/stratification, and the analysis
    of sample-level batch effects. Each "multicellular process" that is extracted 
    consists of (A) a multi cell type gene loadings matrix and (B) a
    corresponding donor scores vector indicating the level at which the corresponding
    loadings matrix is expressed in each donor. Additional methods are implemented
    to aid in selecting an appropriate number of factors and to evaluate stability
    of the decomposition. Additional tools are provided for downstream analysis,
    including integration of gene set enrichment analysis and ligand-receptor analysis. 
    Tucker, L.R. (1966) <doi:10.1007/BF02289464>. Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) <doi:10.1007/s13253-011-0055-9>. Zhou, G., & Cichocki, A. (2012) <doi:10.2478/v10175-012-0051-4>.

Functions to install SciViews additions to R, and more
        tools.

Uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data.

Maximum likelihood estimation of the scaled logit model parameters
    proposed in Dunning (2006) <doi:10.1002/sim.2282>.

Toolbox containing a variety of spectral clustering tools functions. Among the tools available are the hierarchical spectral clustering algorithm, the Shi and Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering algorithm, a multi-level clustering algorithm, recursive clustering and the fast method for all clustering algorithm. As well as other tools needed to run these algorithms or useful for unsupervised spectral clustering. This toolbox aims to gather the main tools for unsupervised spectral classification. See <http://mawenzi.univ-littoral.fr/> for more information and documentation.