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.

Provides methods for distance covariance and distance correlation (Szekely, et al. (2007) <doi:10.1214/009053607000000505>), generalized version thereof (Sejdinovic, et al. (2013) <doi:10.1214/13-AOS1140>) and corresponding tests (Berschneider, Bottcher (2018) <doi:10.48550/arXiv.1808.07280>. Distance standard deviation methods (Edelmann, et al. (2020) <doi:10.1214/19-AOS1935>) and distance correlation methods for survival endpoints (Edelmann, et al. (2021) <doi:10.1111/biom.13470>) are also included.

The 'FBED' and 'mmpc' variable selection algorithms have been implemented using the distance correlation. The references include: Tsamardinos I., Aliferis C. F. and Statnikov A. (2003). "Time and sample efficient discovery of Markovblankets and direct causal relations". In Proceedings of the ninth ACM SIGKDD international Conference. <doi:10.1145/956750.956838>. Borboudakis G. and Tsamardinos I. (2019). "Forward-backward selection with early dropping". Journal of Machine Learning Research, 20(8): 1--39. <doi:10.48550/arXiv.1705.10770>. Huo X. and Szekely G.J. (2016). "Fast computing for distance covariance". Technometrics, 58(4): 435--447. <doi:10.1080/00401706.2015.1054435>.