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Provides a\ngeneric 'R6' class structure that exposes the full tree for\nbuilding custom algorithms, exact Bayesian inference via a\nbottom-up recursive algorithm (closed-form marginal likelihood,\nMaximum A Posteriori (MAP) tree, exact posterior probabilities,\nand exact sampling from the posterior), a frequentist estimator\nvia the context algorithm with likelihood-ratio pruning,\nsimulation utilities, and a Metropolis-Hastings sampler. 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