Query Composite Hypotheses
Gaussian copula density for each H-configuration.
EM calibration in the case of the Gaussian copula (unsigned) with memo...
EM calibration in the case of the Gaussian copula (unsigned)
EM calibration in the case of conditional independence with memory man...
EM calibration in the case of conditional independence
Signed case function: Separate f1 into f+ and f-
FastKerFdr signed
FastKerFdr unsigned
Computation of the sum sum_c(w_c*psi_c) using Gaussian copula parallel...
Computation of the sum sum_c(w_c*psi_c) parallelized version
Gaussian copula density
Specify the configurations corresponding to the composite test "...
Specify the configurations corresponding to the composite test "...
Generate the / configurations.
Update of the prior estimate in EM algo parallelized version
Update of the prior estimate in EM algo using Gaussian copula, paralle...
qch: Query Composite Hypotheses
Infer posterior probabilities of / configurations.
Perform composite hypothesis testing.
Update the estimate of R correlation matrix of the gaussian copula, pa...
Check the Gaussian copula correlation matrix Maximum Likelihood estima...
Gaussian copula correlation matrix Maximum Likelihood estimator (memor...
Gaussian copula correlation matrix Maximum Likelihood estimator.
Provides functions for the joint analysis of Q sets of p-values obtained for the same list of items. This joint analysis is performed by querying a composite hypothesis, i.e. an arbitrary complex combination of simple hypotheses, as described in Mary-Huard et al. (2021) <doi:10.1093/bioinformatics/btab592> and De Walsche et al.(2023) <doi:10.1101/2024.03.17.585412>. In this approach, the Q-uplet of p-values associated with each item is distributed as a multivariate mixture, where each of the 2^Q components corresponds to a specific combination of simple hypotheses. The dependence between the p-value series is considered using a Gaussian copula function. A p-value for the composite hypothesis test is derived from the posterior probabilities.