Converts NIfTI format T1/FL neuroimages into structured,
    high-dimensional 2D data frames with a focus on region of interest
    (ROI) based processing. The package incorporates the partition
    algorithm, which offers a flexible framework for agglomerative
    partitioning based on the Direct-Measure-Reduce approach. This
    method ensures that each reduced variable maintains a user-specified
    minimum level of information while remaining interpretable, as each
    maps uniquely to one variable in the reduced dataset. The
    partition framework is described in Millstein et al. (2020)
    <doi:10.1093/bioinformatics/btz661>. The package allows
    customization in variable selection, measurement of information
    loss, and data reduction methods for neuroimaging analysis and
    machine learning workflows.

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>.