A                       Computes the Adjacency linear operator which
                        maps a vector of weights into a valid Adjacency
                        matrix.
Astar                   Computes the Astar operator.
D                       Computes the degree operator from the vector of
                        edge weights.
Dstar                   Computes the Dstar operator, i.e., the adjoint
                        of the D operator.
L                       Computes the Laplacian linear operator which
                        maps a vector of weights into a valid Laplacian
                        matrix.
Lstar                   Computes the Lstar operator.
accuracy                Computes the accuracy between two matrices
block_diag              Constructs a block diagonal matrix from a list
                        of square matrices
cluster_k_component_graph
                        Cluster a k-component graph from data using the
                        Constrained Laplacian Rank algorithm Cluster a
                        k-component graph on the basis of an observed
                        data matrix. Check out
                        https://mirca.github.io/spectralGraphTopology
                        for code examples.
fdr                     Computes the false discovery rate between two
                        matrices
fscore                  Computes the fscore between two matrices
learn_bipartite_graph   Learn a bipartite graph Learns a bipartite
                        graph on the basis of an observed data matrix
learn_bipartite_k_component_graph
                        Learns a bipartite k-component graph Jointly
                        learns the Laplacian and Adjacency matrices of
                        a graph on the basis of an observed data matrix
learn_combinatorial_graph_laplacian
                        Learn the Combinatorial Graph Laplacian from
                        data Learns a graph Laplacian matrix using the
                        Combinatorial Graph Laplacian (CGL) algorithm
                        proposed by Egilmez et. al. (2017)
learn_graph_sigrep      Learn graphs from a smooth signal
                        representation approach This function learns a
                        graph from a observed data matrix using the
                        method proposed by Dong (2016).
learn_k_component_graph
                        Learn the Laplacian matrix of a k-component
                        graph Learns a k-component graph on the basis
                        of an observed data matrix. Check out
                        https://mirca.github.io/spectralGraphTopology
                        for code examples.
learn_laplacian_gle_admm
                        Learn the weighted Laplacian matrix of a graph
                        using the ADMM method
learn_laplacian_gle_mm
                        Learn the weighted Laplacian matrix of a graph
                        using the MM method
learn_smooth_approx_graph
                        Learns a smooth approximated graph from an
                        observed data matrix. Check out
                        https://mirca.github.io/spectralGraphTopology
                        for code examples.
learn_smooth_graph      Learn a graph from smooth signals This function
                        learns a connected graph given an observed
                        signal matrix using the method proposed by
                        Kalofilias (2016).
npv                     Computes the negative predictive value between
                        two matrices
recall                  Computes the recall between two matrices
relative_error          Computes the relative error between the true
                        and estimated matrices
specificity             Computes the specificity between two matrices
spectralGraphTopology-package
                        Package spectralGraphTopology
