Parses financial condition and performance data (Call Reports) for
    institutions in the United States Farm Credit System. Contains functions for
    downloading files from the Farm Credit Administration (FCA) Call Report
    archive website and reading the files into tidy data frame format.
    The archive website can be found at
    <https://www.fca.gov/bank-oversight/call-report-data-for-download>.

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. Matrix factorization is provided by the GreConD+ algorithm (Belohlavek and Trneckova, 2024 <doi:10.1109/TFUZZ.2023.3330760>).

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.

Bayesian estimation of forced choice models in Item Response Theory using 'rstan' (See Stan Development Team (2020) <https://mc-stan.org/>).