An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.)

Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.

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

Provides an implementation of Secretary Bird Optimization for
    general-purpose continuous optimization, benchmark optimization, and
    training single-hidden-layer feed-forward neural network models. The
    implemented optimizer is based on the Secretary Bird Optimization Algorithm
    proposed by Fu et al. (2024) <doi:10.1007/s10462-024-10729-y>. The neural
    network training functionality is based on Dilber and Özdemir (2026)
    <doi:10.1007/s00521-026-11874-x>.

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.