Performs partial verification bias (PVB) correction for binary 
    diagnostic tests, where PVB arises from selective patient verification in 
    diagnostic accuracy studies. Supports correction of important accuracy
    measures -- sensitivity, specificity, positive predictive values and 
    negative predictive value -- under missing-at-random and 
    missing-not-at-random missing data mechanisms. Available methods and 
    references are "Begg and Greenes' methods" in Alonzo & Pepe (2005) 
    <doi:10.1111/j.1467-9876.2005.00477.x> and deGroot et al. (2011) 
    <doi:10.1016/j.annepidem.2010.10.004>; "Multiple imputation" in 
    Harel & Zhou (2006) <doi:10.1002/sim.2494>, "EM-based logistic regression" 
    in Kosinski & Barnhart (2003) <doi:10.1111/1541-0420.00019>; 
    "Inverse probability weighting" in Alonzo & Pepe (2005) 
    <doi:10.1111/j.1467-9876.2005.00477.x>; "Inverse probability bootstrap 
    sampling" in Nahorniak et al. (2015) <doi:10.1371/journal.pone.0131765> 
    and Arifin & Yusof (2022) <doi:10.3390/diagnostics12112839>; 
    "Scaled inverse probability resampling methods" in Arifin & Yusof (2025) 
    <doi:10.1371/journal.pone.0321440>.

Computes nonparametric p-values for the potential class
        memberships of new observations as well as cross-validated
        p-values for the training data. The p-values are based on
        permutation tests applied to an estimated Bayesian likelihood
        ratio, using a plug-in statistic for the Gaussian model, 'k
        nearest neighbors', 'weighted nearest neighbors' or
        'penalized logistic regression'.
        Additionally, it provides graphical displays and quantitative
        analyses of the p-values.

An implementation of multiscale bootstrap resampling for
             assessing the uncertainty in hierarchical cluster analysis.
             It provides SI (selective inference) p-value, AU (approximately unbiased)
             p-value and BP (bootstrap probability) value for each cluster in a dendrogram.