This is a compendium of 'C++' routines useful for Bayesian statistics. We steal 
  other people's 'C++' code, repurpose it, and export it so developers of 'R' 
  packages can use it in their 'C++' code. We actually don't steal anything, or 
  claim that Thomas Bayes did, but copy code that is compatible with our GPL 3 
  licence, fully acknowledging the authorship of the original code.

The steepness package computes steepness as a
        property of dominance hierarchies. Steepness is defined as the
        absolute slope of the straight line fitted to the normalized
        David's scores. The normalized David's scores can be obtained
        on the basis of dyadic dominance indices corrected for chance
        or by means of proportions of wins. Given an observed
        sociomatrix, it computes hierarchy's steepness and estimates
        statistical significance by means of a randomization test.

Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004)  <DOI:10.1198/016214504000000430>.

Provides Stein-discrepancy goodness-of-fit tests and
    Stein-method-based sampling tools. The tests include kernel Stein
    discrepancy U- and V-statistics following Liu et al. (2016)
    <doi:10.48550/arXiv.1602.03253> and Chwialkowski et al. (2016)
    <doi:10.48550/arXiv.1602.02964>, plus the finite set Stein discrepancy
    test of Jitkrittum et al. (2017) <doi:10.48550/arXiv.1705.07673>. The
    sampling tools include Stein thinning, Stein Points, Stein Point Markov
    chain Monte Carlo, and Stein variational gradient descent following
    Riabiz et al. (2022) <doi:10.48550/arXiv.2005.03952>, Chen et al. (2018)
    <doi:10.48550/arXiv.1803.10161>, Chen et al. (2019)
    <doi:10.48550/arXiv.1905.03673>, and Liu and Wang (2016)
    <doi:10.48550/arXiv.1608.04471>. Gaussian mixture utilities are included
    for simulation, likelihoods, posterior probabilities, scores, and plots.