Functions for the creation/generation and analysis of multilayer social networks <doi:10.18637/jss.v098.i08>.

Analyze multilevel networks as described in Lazega et al (2008)
    <doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders 
    (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an 
    extension to 'igraph'.

Network meta-analysis and network meta-regression models for 
    aggregate data, individual patient data, and mixtures of both individual 
    and aggregate data using multilevel network meta-regression as described by
    Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a
    Bayesian framework using 'Stan'.

Simulating data and fitting multi-species N-mixture models using 'nimble'. Includes features for handling zero-inflation and temporal correlation, Bayesian inference, model diagnostics, parameter estimation, and predictive checks. Designed for ecological studies with zero-altered or time-series data. Mimnagh, N., Parnell, A., Prado, E., & Moral, R. A. (2022) <doi:10.1007/s10651-022-00542-7>. Royle, J. A. (2004) <doi:10.1111/j.0006-341X.2004.00142.x>.

Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) <DOI:10.1007/s11634-023-00547-5>. These models are estimated under  a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>. In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>.