Copula.Hconfig_gaussian_density
                        Gaussian copula density for each
                        H-configuration.
EM_calibration_gaussian
                        EM calibration in the case of the Gaussian
                        copula (unsigned)
EM_calibration_gaussian_memory
                        EM calibration in the case of the Gaussian
                        copula (unsigned) with memory management
EM_calibration_indep    EM calibration in the case of conditional
                        independence
EM_calibration_indep_memory
                        EM calibration in the case of conditional
                        independence with memory management (unsigned)
FastKerFdr_signed       FastKerFdr signed
FastKerFdr_unsigned     FastKerFdr unsigned
GetH1AtLeast            Specify the configurations corresponding to the
                        composite H_1 test "AtLeast".
GetH1Equal              Specify the configurations corresponding to the
                        composite H_1 test "Equal".
GetHconfig              Generate the H_0/H_1 configurations.
PvalSets                Synthetic example to illustrate the main qch
                        functions
PvalSets_cor            Synthetic example to illustrate the main qch
                        functions using Gaussian copula
R.MLE                   Gaussian copula correlation matrix Maximum
                        Likelihood estimator.
R.MLE.check             Check the Gaussian copula correlation matrix
                        Maximum Likelihood estimator
R.MLE.memory            Gaussian copula correlation matrix Maximum
                        Likelihood estimator (memory handling)
R_MLE_update_gaussian_copula_ptr_parallel
                        Update the estimate of R correlation matrix of
                        the gaussian copula, parallelized version
f1_separation_signed    Signed case function: Separate f1 into f+ and
                        f-
fHconfig_sum_update_gaussian_copula_ptr_parallel
                        Computation of the sum sum_c(w_c*psi_c) using
                        Gaussian copula parallelized version
fHconfig_sum_update_ptr_parallel
                        Computation of the sum sum_c(w_c*psi_c)
                        parallelized version
gaussian_copula_density
                        Gaussian copula density
last_incomplete_trapezoid_arma
                        This function is a re-implementation of the
                        initial R loop computing last incomplete
                        trapezoid. See R function
                        integral.kde_adapted().
prior_update_arma_ptr_parallel
                        Update of the prior estimate in EM algo
                        parallelized version
prior_update_gaussian_copula_ptr_parallel
                        Update of the prior estimate in EM algo using
                        Gaussian copula, parallelized version
qch.fit                 Infer posterior probabilities of H_0/H_1
                        configurations.
qch.test                Perform composite hypothesis testing.
remove_decreasing_values_cpp
                        Same as function above but does not handle the
                        index ordering of the vector q. Therefore, the
                        2nd argument order_q has to be an index ordered
                        version of the vector q. Indeed, the R base
                        function: order() is twice as fast as the
                        arma::sort_index(q) This is therefore the
                        recommended function to use.
remove_decreasing_values_cpp_slow_ordering
                        This function is a re-implementation of the
                        initial R side while loop. See the end of R
                        function integral.kde_adapted(). As shown in
                        the commentary below, it is twice as slow to
                        handle the index ordering of the vector q (2nd
                        argument) here with the function
                        arma::sort_index(). Consequently, it is
                        recommended to use the function
                        remove_decreasing_values_cpp() instead.
