Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.

Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures.
              This package utilizes 'runjags' to run Gibbs sampling with parallel chains.
              Options for different covariances/relationship structures between parameters of interest.

Performs 'BTLLasso' as described by Schauberger and Tutz (2019) <doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.

Biterm Topic Models find topics in collections of short texts. 
    It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms.
    This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis 
    which are word-document co-occurrence topic models.
    A biterm consists of two words co-occurring in the same short text window.  
    This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. 
    The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) <https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>.