Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.

An efficient cross-validated approach for covariance matrix
    estimation, particularly useful in high-dimensional settings. This
    method relies upon the theory of high-dimensional loss-based covariance
    matrix estimator selection developed by Boileau et al. (2022)
    <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator
    from among a prespecified set of candidates.

Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> 
            is suitable for cluster randomized trials (CRTs) with a
            small number of clusters (e.g., 20 or fewer). The procedure of
            constrained randomization is based on the baseline values of some
            cluster-level covariates specified. The intervention effect on
            the individual outcome can then be analyzed through
            clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. 
            Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline
            values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.

Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation.