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.

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

A financial calculator that provides very fast implementations
    of common financial indicators using 'Rust' code. It includes functions for
    bond-related indicators, such as yield to maturity ('YTM'), modified duration,
    and Macaulay duration, as well as functions for calculating time-weighted
    and money-weighted rates of return (using 'Modified Dietz' method) for multiple portfolios,
    given their market values and profit and loss ('PnL') data. 'fcl' is designed
    to be efficient and accurate for financial analysis and computation. The methods
    used in this package are based on the following references:
    <https://en.wikipedia.org/wiki/Modified_Dietz_method>,
    <https://en.wikipedia.org/wiki/Time-weighted_return>.