Likelihood-free inference method for stochastic models.
    Uses a deterministic optimizer on simple simulations of the model
    that are performed with a prior drawn randomness by applying the inverse transform method.
    Is designed to work on its own and also by using the Julia package 'Jflimo'
    available on the git page of the project: <https://metabarcoding.org/flimo>.

An R interface to 'FLINT' <https://flintlib.org/>, a C library for
	number theory.  'FLINT' extends GNU 'MPFR' <https://www.mpfr.org/>
	and GNU 'MP' <https://gmplib.org/> with support for operations on
	standard rings (the integers, the integers modulo n, finite
	fields, the rational, p-adic, real, and complex numbers) as well
	as matrices and polynomials over rings.  'FLINT' implements
	midpoint-radius interval arithmetic, also known as ball
	arithmetic, in the real and complex numbers, enabling computation
	in arbitrary precision with rigorous propagation of rounding and
	other errors; see Johansson (2017) <doi:10.1109/TC.2017.2690633>.
	Finally, 'FLINT' provides ball arithmetic implementations of many
	special mathematical functions, with high coverage of reference
	works such as the NIST Digital Library of Mathematical Functions
	<https://dlmf.nist.gov/>.  The R interface defines S4 classes,
	generic functions, and methods for representation and basic
	operations as well as plain R functions mirroring and vectorizing
	entry points in the C library.

Given a multivariate dataset and some knowledge about the dependencies 
              between its features, it is customary to fit a statistical model to the features 
              to infer parameters of interest. Such a procedure implicitly assumes that the 
              sample is exchangeable. This package provides a flexible non-parametric test 
              of this exchangeability assumption, allowing the user to specify the feature 
              dependencies by hand as long as features can be grouped into disjoint independent sets. 
              This package also allows users to test a dual hypothesis, which is, given that the 
              sample is exchangeable, does a proposed grouping of the features into disjoint sets
              also produce statistically independent sets of features? See Aw, Spence and Song (2023) 
              for the accompanying paper.