Statistical Computing

Estimating complex network models requires efficient and flexible algorithms. Their use by non-statisticians requires flexible and intuitive user interfaces.

Related papers

Pavel N. Krivitsky, David R. Hunter, Martina Morris, and Chad Klumb, ergm 4: New features for analyzing exponential-family random graph models, Journal of Statistical Software, vol. 105, no. 6, pp. 1–44, 2023. doi:10.18637/jss.v105.i06

Pavel N. Krivitsky, Alina R. Kuvelkar, and David R. Hunter, Likelihood-based inference for exponential-family random graph models via linear programming, Electronic Journal of Statistics, vol. 17, no. 2, 2023. doi:10.1214/23-EJS2176

Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts, and Jonathan R. Stewart, Exponential-family models of random graphs: Inference in finite, super and infinite population scenarios, Statistical Science, vol. 35, no. 4, pp. 627–662, 2020. doi:10.1214/19-STS743

Pavel N. Krivitsky, Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models, Computational Statistics & Data Analysis, vol. 107, pp. 149–161, 2017. doi:10.1016/j.csda.2016.10.015

Nicole Bohme Carnegie, Pavel N. Krivitsky, David R. Hunter, and Steven M. Goodreau, An approximation method for improving dynamic network model fitting, Journal of Computational and Graphical Statistics, vol. 24, no. 2, pp. 502–519, 2015. doi:10.1080/10618600.2014.903087

David R. Hunter, Pavel N. Krivitsky, and Michael Schweinberger, Computational statistical methods for social network models, Journal of Computational and Graphical Statistics, vol. 21, no. 4, pp. 856–882, 2012. doi:10.1080/10618600.2012.732921

Pavel N. Krivitsky and Mark S. Handcock, Fitting position latent cluster models for social networks with, Journal of Statistical Software, vol. 24, no. 5, pp. 1–23, 2008. doi:10.18637/jss.v024.i05