Provides functions for specifying and fitting nested
    dichotomy logistic regression models for a multi-category response and
    methods for summarising and plotting those models.  Nested dichotomies are
    statistically independent, and hence provide an additive decomposition
    of tests for the overall 'polytomous' response.  When the dichotomies
    make sense substantively, this method can be a simpler alternative to
    the standard 'multinomial' logistic model which compares response
    categories to a reference level.  See: J. Fox (2016), "Applied
    Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN
    1452205663.

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.

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.