Builds and interprets multi-response machine learning models using 'tidymodels' syntax. Users can supply a tidy model, and 'mrIML' automates the process of fitting multiple response models to multivariate data and applying interpretable machine learning techniques across them. For more details see Fountain-Jones (2021) <doi:10.1111/1755-0998.13495> and Fountain-Jones et al. (2024) <doi:10.22541/au.172676147.77148600/v1>.

Implements various methods for tissue classification in magnetic
        resonance (MR) images of the brain, including normal mixture models
        and hidden Markov normal mixture models, as outlined in Feng &
        Tierney (2011) <doi:10.18637/jss.v044.i07>. These methods allow a
        structural MR image to be classified into gray matter, white matter
        and cerebrospinal fluid tissue types.

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>.

The goal of 'MRMCbinary' is to compare the performance of diagnostic tests (i.e., sensitivity and specificity) for binary outcomes in multi-reader multi-case (MRMC) studies. It is based on conditional logistic regression and Cochran’s Q test (or McNemar’s test when the number of modalities is equal to 2).