Provides access to a range of functions for computing and visualizing the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative, and the Full Bayesian Evidence Test (FBET) and the (generalized) Bayesian evidence value for testing a composite (or interval) hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available.

Provides a streamlined, standard evaluation-based approach to multivariate function composition. Allows for chaining commands via a forward-pipe operator, %>%.

Perform various floating catchment area methods to calculate a
    spatial accessibility index (SPAI) for demand point data. The distance
    matrix used for weighting is normalized in a preprocessing step using
    common functions (gaussian, gravity, exponential or logistic).

Provides tools to perform fuzzy formal concept
    analysis, presented in Wille (1982) <doi:10.1007/978-3-642-01815-2_23>
    and in Ganter and Obiedkov (2016) <doi:10.1007/978-3-662-49291-8>.  It
    provides functions to load and save a formal context, extract its
    concept lattice and implications.  In addition, one can use the
    implications to compute semantic closures of fuzzy sets and, thus,
    build recommendation systems.

Provides support for building Feldman-Cousins confidence intervals 
	[G. J. Feldman and R. D. Cousins (1998) <doi:10.1103/PhysRevD.57.3873>].

Provides functions to implement the Flexible cFDR (Hutchinson et al. (2021) <doi:10.1371/journal.pgen.1009853>) and Binary cFDR (Hutchinson et al. (2021) <doi:10.1101/2021.10.21.465274>) methodologies to leverage auxiliary data from arbitrary distributions, for example functional genomic data, with GWAS p-values to generate re-weighted p-values.

Analyze functional data and its change points. Includes 
    functionality to store and process data, summarize and validate assumptions, 
    characterize and perform inference of change points, and provide 
    visualizations. Data is stored as discretely collected observations without
    requiring the selection of basis functions. For more details see chapter 8 
    of Horvath and Rice (2024) <doi:10.1007/978-3-031-51609-2>. Additional papers
    are forthcoming. Focused works are also included in the documentation of 
    corresponding functions.