An estimation method that can use computer simulations to
    approximate maximum-likelihood estimates even when the likelihood function can not
    be evaluated directly. It can be applied whenever it is feasible to conduct many
    simulations, but works best when the data is approximately Poisson distributed.
    It was originally designed for demographic inference in evolutionary
    biology (Naduvilezhath et al., 2011 <doi:10.1111/j.1365-294X.2011.05131.x>,
    Mathew et al., 2013 <doi:10.1002/ece3.722>).
    It has optional support for conducting coalescent simulation using
    the 'coala' package.

Provides data about the possible adverse events/reactions
    resulting from being injected with a vaccine/experimental gene therapy.
    Currently, this data set only includes information from six reference
    sources. Refer to the CITATION.cff file for the complete citations of
    the reference sources. For information about vaccination$/immunization$
    hazards, visit <https://www.questionuniverse.com/rethink.html#vaccine>,
    <https://www.ecoccs.com/healing.html#vaccines>,
    <https://www.questionuniverse.com/rethink_current_crisis.html#cov_vaccin>,
    and <https://www.questionuniverse.com/vaccination.html>.

Calculate statistical significance of Jaccard/Tanimoto similarity
    coefficients for binary data.

Schur polynomials appear in combinatorics and zonal
    polynomials appear in random matrix theory. They are particular cases
    of Jack polynomials. This package allows to compute these polynomials
    and other symmetric multivariate polynomials: flagged Schur
    polynomials, factorial Schur polynomials, t-Schur polynomials,
    Hall-Littlewood polynomials, Macdonald polynomials, and modified
    Macdonald polynomials. In addition, it can compute the Kostka-Jack
    numbers, the Kostka-Foulkes polynomials, the Kostka-Macdonald
    polynomials, and the Hall polynomials.  Mainly based on Demmel &
    Koev's paper (2006) <doi:10.1090/S0025-5718-05-01780-1> and
    Macdonald's book (1995) <doi:10.1093/oso/9780198534891.003.0001>.