Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.

Generate Stochastic Branching Networks ('SBNs'). Used to model the branching structure of rivers.

Utilities for training and evaluating text predictors based on Stupid Back-Off N-gram models (Brants et al., 2007, <https://www.aclweb.org/anthology/D07-1090/>).

Creates classifier for binary outcomes using Adaptive Boosting 
    (AdaBoost) algorithm on decision stumps with a fast C++ implementation. 
    For a description of AdaBoost, see Freund and Schapire (1997) 
    <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but
    easy to interpret and visualize. Feature vectors may be a combination of
    continuous (numeric) and categorical (string, factor) elements. Methods 
    for classifier assessment, predictions, and cross-validation also included.

An efficient implementation of Scalable Bayesian Rule Lists Algorithm, a competitor algorithm for decision tree algorithms; see Hongyu Yang, Cynthia Rudin, Margo Seltzer (2017) <https://proceedings.mlr.press/v70/yang17h.html>. It builds from pre-mined association rules and have a logical structure identical to a decision list or one-sided decision tree. Fully optimized over rule lists, this algorithm strikes practical balance between accuracy, interpretability, and computational speed.

Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with 'Mplus' software, specifically when implementing the complex survey option. In such cases, the model estimator in 'Mplus' defaults to ML with robust standard errors.