'C++' classes for sparse matrix methods including implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) 
  <https://fossies.org/linux/SuiteSparse/LDL/Doc/ldl_userguide.pdf>. Provides a set of C++ classes for basic sparse
  matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See <https://github.com/samuel-watson/SparseChol>
  for details.

Implements spectral clustering algorithms for community detection
    in sparse networks under the stochastic block model ('SBM') and
    degree-corrected stochastic block model ('DCSBM'), following the methods of
    Lei and Rinaldo (2015) <doi:10.1214/14-AOS1274>. Provides a regularized
    normalized Laplacian embedding, spherical k-median clustering for 'DCSBM',
    standard k-means for 'SBM', simulation utilities for both models, and a
    misclustering rate evaluation metric. Also includes the 'NCAA' college
    football network of Girvan and Newman (2002) <doi:10.1073/pnas.122653799>
    as a benchmark dataset, and the Bethe-Hessian community number estimator
    of Hwang (2023) <doi:10.1080/01621459.2023.2223793>.

A sparse covariance estimator based on different thresholding operators.

Implements the algorithm described in 
    Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential
    clustering algorithm for tracing cell type changes via single-cell
    RNA-sequencing data", Nucleic Acids Research, gkx1113,
    <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two
    different populations, links the clusters across the conditions and
    identifies marker genes for these changes. The package was designed for
    scRNA-Seq data but is also applicable to many other data types, just
    replace cells with samples and genes with variables. The package also
    contains functions for estimating the parameters for SparseDC as outlined
    in the paper. We recommend that users further select their marker genes
    using the magnitude of the cluster centers.