Functions to accompany Wayne W. Daniel's Biostatistics: A
        Foundation for Analysis in the Health Sciences, Tenth Edition.

Discriminant Adaptive Nearest Neighbor Classification is a 
    variation of k nearest neighbors where the shape of the neighborhood is 
    data driven. This package implements dann and sub_dann from
    Hastie (1996) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.

Calculates Days Alive and Out of Hospital (DAOH) from administrative
    admission/discharge/mortality data using three algorithms (nights, days, exact)
    and three death-handling approaches (midday, midnight, zero).
    Includes tools for comparing methods (Bland-Altman, ICC, reclassification), and plotting.

A data augmentation based sampler for conducting privacy-aware Bayesian inference. The dapper_sample()
             function takes an existing sampler as input and automatically constructs
             a privacy-aware sampler. The process of constructing a sampler is simplified 
             through the specification of four independent modules, allowing for
             easy comparison between different privacy mechanisms by only swapping
             out the relevant modules. Probability mass functions
             for the discrete Gaussian and discrete Laplacian are provided to facilitate
             analyses dealing with privatized count data. The output of dapper_sample()
             can be analyzed using many of the same tools from the 'rstan' ecosystem. For methodological details
             on the sampler see Ju et al. (2022) <doi:10.48550/arXiv.2206.00710>,
             and for details on the discrete Gaussian and discrete Laplacian distributions see
             Canonne et al. (2020) <doi:10.48550/arXiv.2004.00010>.

Provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package 'bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...).