ORCID as entered in ROS

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2022, ergm 4: Computational Improvements, , http://dx.doi.org/10.48550/arxiv.2203.08198
,2022, Modeling of Dynamic Networks based on Egocentric Data with Durational Information, , http://dx.doi.org/10.48550/arxiv.2203.06866
,2022, Modeling Tie Duration in ERGM-Based Dynamic Network Models, , http://dx.doi.org/10.48550/arxiv.2203.11817
,2022, A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks, , http://dx.doi.org/10.48550/arxiv.2202.03685
,2022, Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming, , http://dx.doi.org/10.48550/arxiv.2202.03572
,2021, ergm 4: New features, , http://dx.doi.org/10.48550/arxiv.2106.04997
,2021, Bayesian graph convolutional neural networks via tempered MCMC, , http://dx.doi.org/10.48550/arxiv.2104.08438
,2021, Revisiting Bayesian Autoencoders with MCMC, , http://dx.doi.org/10.48550/arxiv.2104.05915
,2019, Exponential-Family Random Graph Models for Multi-Layer Networks, , http://dx.doi.org/10.31235/osf.io/dqe9b
,2017, Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios, , http://dx.doi.org/10.48550/arxiv.1707.04800
,2017, A note on the role of projectivity in likelihood-based inference for random graph models, , http://dx.doi.org/10.48550/arxiv.1707.00211
,2015, Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models, , http://dx.doi.org/10.48550/arxiv.1511.02930
,2015, Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict, , http://dx.doi.org/10.48550/arxiv.1507.08401
,2014, Differentially Private Exponential Random Graphs, , http://dx.doi.org/10.48550/arxiv.1409.4696
,2012, Exponential-Family Random Graph Models for Rank-Order Relational Data, , http://dx.doi.org/10.48550/arxiv.1210.0493
,2011, On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry, , http://dx.doi.org/10.48550/arxiv.1112.0840
,2011, Exponential-Family Random Graph Models for Valued Networks, , http://dx.doi.org/10.48550/arxiv.1101.1359
,2010, A Separable Model for Dynamic Networks, , http://dx.doi.org/10.48550/arxiv.1011.1937
,2010, Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models, , http://dx.doi.org/10.48550/arxiv.1004.5328
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