A collection of functions for calculating the M2 model fit
    statistic for diagnostic classification models as described by Liu et al.
    (2016) <DOI:10.3102/1076998615621293>. These functions provide multiple
    sources of information for model fit according to the M2 statistic,
    including the M2 statistic, the *p* value for that M2 statistic, and the
    Root Mean Square Error of Approximation based on the M2 statistic.

Access data sets for demonstrating or testing diagnostic
    classification models. Simulated data sets can be used to compare estimated
    model output to true data-generating values. Real data sets can be used to
    demonstrate real-world applications of diagnostic models.

S4 classes around infrastructure provided by the
    'coda' and 'dclone' packages to make package development easy as a breeze
    with data cloning for hierarchical models.

Data cleaning scripts typically contain a lot of 'if this change that'
    type of statements. Such statements are typically condensed expert knowledge.
    With this package, such 'data modifying rules' are taken out of the code and
    become in stead parameters to the work flow. This allows one to maintain, document,
    and reason about data modification rules as separate entities.

Diagnostic classification models are psychometric models used
    to categorically estimate respondents mastery, or proficiency, on a
    set of predefined skills (Bradshaw, 2016,
    <doi:10.1002/9781118956588.ch13>).  Diagnostic models can be estimated
    with 'Stan'; however, the necessary scripts can be long and
    complicated. This package automates the creation of 'Stan' scripts for
    diagnostic classification models. Specify different types of
    diagnostic models, define prior distributions, and automatically
    generate the necessary 'Stan' code for estimating the model.

Independent component analysis based on distance correlation, including a robust variant using the bowl transformation. The package provides user-facing implementations of distance covariance and distance correlation, including memory-efficient blockwise computations for large data sets. It includes a sequential ICA estimator based on minimizing distance correlation, as well as tools for analyzing serial dependence via distance autocorrelation, dependograms, and permutation-based tests. In addition, it provides functions for testing serial dependence based on distance correlation and the Hilbert–Schmidt independence criterion. The methodology is related to Matteson and Tsay (2017) <doi:10.1080/01621459.2016.1150851> and to the robust framework of Leyder et al. (2026) <doi:10.1007/s11634-026-00674-9>.