R: Query Composite Hypotheses

# Query Composite Hypotheses

## Documentation for package ‘qch’ version 2.1.3

- DESCRIPTION file

## Help Pages

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)
f1_separation_signed | Signed case function: Separate f1 into f+ and f-
FastKerFdr_signed | FastKerFdr signed
FastKerFdr_unsigned | FastKerFdr unsigned
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
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.
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
PvalSets | Synthetic example to illustrate the main qch functions
PvalSets_cor | Synthetic example to illustrate the main qch functions using Gaussian copula
qch.fit | Infer posterior probabilities of H_0/H_1 configurations.
qch.test | Perform composite hypothesis testing.
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)
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.
R_MLE_update_gaussian_copula_ptr_parallel | Update the estimate of R correlation matrix of the gaussian copula, parallelized version