Estimation and comparison of the performances of diagnostic tests
    in multi-reader multi-case studies where true case statuses (or ground
    truths) are known and one or more readers provide test ratings for multiple
    cases.  Reader performance metrics are provided for area under and expected
    utility of ROC curves, likelihood ratio of positive or negative tests, and
    sensitivity and specificity.  ROC curves can be estimated empirically or
    with binormal or binormal likelihood-ratio models.  Statistical comparisons
    of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and
    the unified framework of Hillis (2005) <doi:10.1002/sim.2024>.  The ANOVA
    can be conducted with data from a full factorial, nested, or partially
    paired study design; with random or fixed readers or cases; and covariances
    estimated with the DeLong method, jackknifing, or an unbiased method.  Smith
    and Hillis (2020) <doi:10.1117/12.2549075>.

Sample size estimations for MRMC studies based on the Obuchowski-Rockette (OR) methodology is implemented. The function can calculate sample sizes where the 
             endpoint of interest in the study is either ROC AUC (Area-Under-the-Receiver-Operating-Characteristics-Curve) or sensitivity. The package can also return sample sizes for studies
             expected to have clustering effect (e.g.- multiple pulmonary nodules per patient). All calculations assume that the study design is fully crossed (paired-reader, paired-case) where
             each reader reads/interprets each case and that there are two interventions/imaging-modalities/techniques in the study. In addition to MRMC, it can also be used to estimate sample sizes 
             for standalone studies where sensitivity or AUC are the primary endpoints.
             The methods implemented are based on the methods described in Zhou et.al. (2011) <doi:10.1002/9780470906514> and Obuchowski (2000) <doi:10.1097/EDE.0b013e3181a663cc>.

A causal mediation approach under the counterfactual framework 
    to test the significance of total, direct and indirect effects. In this approach, 
    a group of methylated sites from a predefined region are utilized as the mediator, 
    and the functional transformation is used to reduce the possible high dimension 
    in the region-based methylated sites and account for their location information.