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BoltzMM: an R package for maximum pseudolikelihood estimation of fully-visible Boltzmann machines

The FVBM model can be viewed as a multivariate extension of the classical Bernoulli distribution, on spin-binary random variable strings (i.e, random variables X ∈ X, rather X ∈ {0, 1}), and can be demonstrated to be equivalent to the logistic multivariate binary model that was proposed by Cox (1972). It can also be considered as a fullyconnected binary graphical model (see Bagnall et al., 2018 for more details). Simply put, the FVBM is a model that allows for the characterization of interactions between multiple binary random variables, simultaneously.