admm_algorithm          Performs the Alternating Descent Method of
                        Multipliers (ADMM) algorithm to estimate the
                        LoRI parameters.
aravo                   Aravo
default_projection      Default projection on covariate space: center
                        by row and column.
estimate_null_model     Estimates the LoRI model under the constraint
                        Theta = 0.
gradient                Computes the gradient of the objective function
                        in X for gradient descent step in ADMM.
lambda_QUT              Computes the threshold $lambda_QUT$ with
                        parametric bootstrap when NO covariates are
                        available. If you don't have any covariates,
                        use this function instead of
                        'lambda_QUT_covariates' which will be
                        significantly slower.
lambda_QUT_covariates   Computes the threshold $lambda_QUT$ with
                        parametric bootstrap when covariates are
                        available. If you don't have any covariates,
                        use the function 'lambda_QUT' which will be
                        significantly faster.
lambda_cv               Selects the parameter $lambda_CV$ with
                        cross-validation.
lori                    Main function to be used to fit the LORI model
objective_function      Computes the value of the objective function in
                        X to be optimized at each iteration of ADMM.
plot_interaction        Plot the rows and columns of the contingency
                        table in the Euclidean space defined by two of
                        the first principal directions of the
                        interaction matrix Theta. Theta corresponds to
                        the interaction remaining after discarding the
                        effects of the covariates. The interpretation
                        is the following. A row and a column that are
                        close in Euclidean distance interact highly.
                        Two rows or two columns that are close in
                        Euclidean distance have similar profiles.
