Provides a nested menu widget for usage in 'Shiny'
    applications. This is useful for hierarchical choices (e.g. continent,
    country, city).

Library to plot performance profiles (Dolan and More (2002) <doi:10.1007/s101070100263>) and nested performance profiles (Hekmati and Mirhajianmoghadam (2019) <doi:10.19139/soic-2310-5070-679>) for a given data frame.

Implementation of forward selection based on cross-validated
             linear and logistic regression.

Estimate, compare, and analyze dynamic and psychological networks
    using a unified interface. Provides transition network analysis
    estimation (transition, frequency, co-occurrence, attention-weighted)
    Saqr et al. (2025) <doi:10.1145/3706468.3706513>, psychological
    network methods (correlation, partial correlation, 'graphical lasso',
    'Ising') Saqr, Beck, and Lopez-Pernas (2024)
    <doi:10.1007/978-3-031-54464-4_19>,
    and higher-order network methods including higher-order networks,
    higher-order network embedding, hyper-path anomaly, and multi-order
    generative model. Supports bootstrap inference, permutation testing,
    split-half reliability, centrality stability analysis, mixed Markov
    models, multi-cluster multi-layer networks and clustering.

This R package provides a calculation of between-cases AUC estimate, corresponding covariance, and variance estimate in the nested data problem.  Also, the package has the function to simulate the nested data. The calculated between-cases AUC estimate is used to evaluate the reader's diagnostic performance in clinical tasks with nested data. For more details on the above methods, please refer to the paper by H Du, S Wen, Y Guo, F Jin, BD Gallas (2022) <doi:10.1177/09622802221111539>.

Facilitates building a nesting or hierarchical structure as a list or data frame by using a human friendly syntax.

Infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.