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

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.

Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of 'netClust'.

Facilitates network clustering and evaluation of cluster
        configurations.

Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of 'JavaScript' in one package.

Infer system functioning with empirical NETwork COMparisons. These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) <doi:10.1038/s41598-021-99251-7>, Langendorf (2020) <doi:10.1201/9781351190831-6>, and Langendorf and Goldberg (2019) <doi:10.48550/arXiv.1912.12551>.

Implementations of various control theory methods for use 
            in brain and psychological networks. Contains controllability
            statistics from Pasqualetti, Zampieri & Bullo (2014) <doi:10.1109/TCNS.2014.2310254>,
            optimal control algorithms from Lewis, Vrabie & Syrmos (2012, ISBN:978-0-470-63349-6), and various utilities.