book.bib

@Book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.name/knitr/},
}

@article{Hunter2008,
  author = {Hunter, David R. and Handcock, Mark S. and Butts, Carter T. and Goodreau, Steven M. and Morris, Martina},
  doi = {10.18637/jss.v024.i03},
  issn = {1548-7660},
  journal = {Journal of Statistical Software},
  number = {3},
  title = {{ergm : A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks}},
  url = {http://www.jstatsoft.org/v24/i03/},
  volume = {24},
  year = {2008}
}

@article{Morris2008,
   author = {Martina Morris and Mark Handcock and David Hunter},
   title = {Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects},
   journal = {Journal of Statistical Software, Articles},
   volume = {24},
   number = {4},
   year = {2008},
   keywords = {},
   abstract = {Exponential-family random graph models (ERGMs) represent the processes that govern the formation of links in networks through the terms selected by the user. The terms specify network statistics that are sufficient to represent the probability distribution over the space of networks of that size. Many classes of statistics can be used.  In this article we describe the classes of statistics that are currently available in the ergm package. We also describe means for controlling the Markov chain Monte Carlo (MCMC) algorithm that the package uses for estimation. These controls affect either the proposal distribution on the sample space used by the underlying Metropolis-Hastings algorithm or the constraints on the sample space itself. Finally, we describe various other arguments to core functions of the ergm package.},
   issn = {1548-7660},
   pages = {1--24},
   doi = {10.18637/jss.v024.i04},
   url = {https://www.jstatsoft.org/v024/i04}
}

@book{Matloff2011,
  title={The art of R programming: A tour of statistical software design},
  author={Matloff, Norman},
  year={2011},
  publisher={No Starch Press}
}

@book{brooks2011,
  title={Handbook of markov chain monte carlo},
  author={Brooks, Steve and Gelman, Andrew and Jones, Galin and Meng, Xiao-Li},
  year={2011},
  publisher={CRC press}
}

@article{HunterJASA2008,
  author = {David R Hunter and Steven M Goodreau and Mark S Handcock},
  title = {Goodness of Fit of Social Network Models},
  journal = {Journal of the American Statistical Association},
  volume = {103},
  number = {481},
  pages = {248-258},
  year  = {2008},
  publisher = {Taylor & Francis},
  doi = {10.1198/016214507000000446},
  URL = {https://doi.org/10.1198/016214507000000446},
  eprint = {https://doi.org/10.1198/016214507000000446}
}

@article{Snijders2002,
  title={Markov chain Monte Carlo estimation of exponential random graph models},
  author={Snijders, Tom AB},
  journal={Journal of Social Structure},
  volume=3,
  year={2002}
}

@article{Wang2009,
  title = "Exponential random graph (p*) models for affiliation networks",
  journal = "Social Networks",
  volume = "31",
  number = "1",
  pages = "12 - 25",
  year = "2009",
  issn = "0378-8733",
  doi = "https://doi.org/10.1016/j.socnet.2008.08.002",
  url = "http://www.sciencedirect.com/science/article/pii/S0378873308000403",
  author = "Peng Wang and Ken Sharpe and Garry L. Robins and Philippa E. Pattison",
  keywords = "Exponential random graph () models, Affiliation networks, MCMC MLE, Partial conditional dependence assumption"
}

@techreport{admiraal2006,
  title={Sequential importance sampling for bipartite graphs with applications to likelihood-based inference},
  author={Admiraal, Ryan and Handcock, Mark S},
  year={2006},
  institution={Department of Statistics, University of Washington}
}

@article{Geyer1992,
 ISSN = {00359246},
 URL = {http://www.jstor.org/stable/2345852},
 abstract = {Maximum likelihood estimates (MLEs) in autologistic models and other exponential family models for dependent data can be calculated with Markov chain Monte Carlo methods (the Metropolis algorithm or the Gibbs sampler), which simulate ergodic Markov chains having equilibrium distributions in the model. From one realization of such a Markov chain, a Monte Carlo approximant to the whole likelihood function can be constructed. The parameter value (if any) maximizing this function approximates the MLE. When no parameter point in the model maximizes the likelihood, the MLE in the closure of the exponential family may exist and can be calculated by a two-phase algorithm, first finding the support of the MLE by linear programming and then finding the distribution within the family conditioned on the support by maximizing the likelihood for that family. These methods are illustrated by a constrained autologistic model for DNA fingerprint data. MLEs are compared with maximum pseudolikelihood estimates (MPLEs) and with maximum conditional likelihood estimates (MCLEs), neither of which produce acceptable estimates, the MPLE because it overestimates dependence, and the MCLE because conditioning removes the constraints.},
 author = {Charles J. Geyer and Elizabeth A. Thompson},
 journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
 number = {3},
 pages = {657--699},
 publisher = {[Royal Statistical Society, Wiley]},
 title = {Constrained Monte Carlo Maximum Likelihood for Dependent Data},
 volume = {54},
 year = {1992}
}

@book{lusher2012,
  title={Exponential random graph models for social networks: Theory, methods, and applications},
  author={Lusher, Dean and Koskinen, Johan and Robins, Garry},
  year={2012},
  publisher={Cambridge University Press}
}

@Manual{R-readr,
  title = {readr: Read Rectangular Text Data},
  author = {Hadley Wickham and Jim Hester and Jennifer Bryan},
  year = {2024},
  note = {R package version 2.1.5, https://github.com/tidyverse/readr},
  url = {https://readr.tidyverse.org},
}

@Manual{R-foreign,
  title = {foreign: Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata',
'Systat', 'Weka', 'dBase', ...},
  author = {{R Core Team}},
  year = {2023},
  note = {R package version 0.8-86},
  url = {https://svn.r-project.org/R-packages/trunk/foreign/},
}



@article{Snijders2010,
abstract = {Stochastic actor-based models are models for network dynamics that can represent a wide variety of influences on network change, and allow to estimate parameters expressing such influences, and test corresponding hypotheses. The nodes in the network represent social actors, and the collection of ties represents a social relation. The assumptions posit that the network evolves as a stochastic process 'driven by the actors', i.e., the model lends itself especially for representing theories about how actors change their outgoing ties. The probabilities of tie changes are in part endogenously determined, i.e., as a function of the current network structure itself, and in part exogenously, as a function of characteristics of the nodes ('actor covariates') and of characteristics of pairs of nodes ('dyadic covariates'). In an extended form, stochastic actor-based models can be used to analyze longitudinal data on social networks jointly with changing attributes of the actors: dynamics of networks and behavior. This paper gives an introduction to stochastic actor-based models for dynamics of directed networks, using only a minimum of mathematics. The focus is on understanding the basic principles of the model, understanding the results, and on sensible rules for model selection. Crown Copyright {\textcopyright} 2009.},
author = {Snijders, Tom A B and van de Bunt, Gerhard G. and Steglich, Christian E G},
doi = {10.1016/j.socnet.2009.02.004},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Snijders, van de Bunt, Steglich - 2010 - Introduction to stochastic actor-based models for network dynamics(2).pdf:pdf},
isbn = {0378-8733},
issn = {03788733},
journal = {Social Networks},
keywords = {Agent-based model,Longitudinal,Markov chain,Peer influence,Peer selection,Statistical modeling},
number = {1},
pages = {44--60},
title = {{Introduction to stochastic actor-based models for network dynamics}},
volume = {32},
year = {2010}
}

@book{lazega2015,
  title={Multilevel network analysis for the social sciences: theory, methods and applications},
  author={Lazega, Emmanuel and Snijders, Tom AB},
  volume={12},
  year={2015},
  publisher={Springer}
}

@article{Ripley2011,
  author = {Ripley, Ruth M. and Snijders, Tom AB and Preciado, Paulina and Others},
  journal = {University of Oxford: Department of Statistics, Nuffield College},
  number = {2007},
  title = {{Manual for RSIENA}},
  url = {https://www.uni-due.de/hummell/sna/R/RSiena{\_}Manual.pdf},
  year = {2011}
}

@article{Snijders1999,
author = {Snijders, Tom A B and Borgatti, Stephen P},
journal = {Connections},
keywords = {Non-parametric},
mendeley-tags = {Non-parametric},
number = {2},
pages = {1--10},
title = {{Non-Parametric Standard Errors and Tests for Network Statistics}},
url = {https://insna.org/PDF/Connections/v22/1999_I-2_61-70.pdf},
volume = {22},
year = {1999}
}

@book{Efron1994,
  title={An introduction to the bootstrap},
  author={Efron, Bradley and Tibshirani, Robert J},
  year={1994},
  publisher={CRC press}
}

@article{Snijders2010margin,
author = {SNIJDERS, TOM A. B.},
doi = {10.1080/0022250X.2010.485707},
file = {:home/george/Dropbox/papers/s/Snijders (Journal of Mathematical Sociology 2010).pdf:pdf},
issn = {0022-250X},
journal = {The Journal of Mathematical Sociology},
month = {sep},
number = {4},
pages = {239--252},
publisher = {Routledge},
title = {{Conditional Marginalization for Exponential Random Graph Models}},
url = {https://www.tandfonline.com/doi/abs/10.1080/0022250X.2010.485707 http://www.tandfonline.com/doi/abs/10.1080/0022250X.2010.485707},
volume = {34},
year = {2010}
}

@Manual{R-readxl,
  title = {readxl: Read Excel Files},
  author = {Hadley Wickham and Jennifer Bryan},
  year = {2023},
  note = {R package version 1.4.3, https://github.com/tidyverse/readxl},
  url = {https://readxl.tidyverse.org},
}

@Manual{R-tidyr,
  title = {tidyr: Tidy Messy Data},
  author = {Hadley Wickham and Davis Vaughan and Maximilian Girlich},
  year = {2024},
  note = {R package version 1.3.1, https://github.com/tidyverse/tidyr},
  url = {https://tidyr.tidyverse.org},
}

@Manual{R-dplyr,
  title = {dplyr: A Grammar of Data Manipulation},
  author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller and Davis Vaughan},
  year = {2023},
  note = {R package version 1.1.4, https://github.com/tidyverse/dplyr},
  url = {https://dplyr.tidyverse.org},
}

@Manual{R-magrittr,
  title = {magrittr: A Forward-Pipe Operator for R},
  author = {Stefan Milton Bache and Hadley Wickham},
  year = {2022},
  note = {R package version 2.0.3, 
https://github.com/tidyverse/magrittr},
  url = {https://magrittr.tidyverse.org},
}

@Manual{R-stringr,
  title = {stringr: Simple, Consistent Wrappers for Common String Operations},
  author = {Hadley Wickham},
  year = {2023},
  note = {R package version 1.5.1, 
https://github.com/tidyverse/stringr},
  url = {https://stringr.tidyverse.org},
}

@Manual{R-rex,
  title = {rex: Friendly Regular Expressions},
  author = {Kevin Ushey and Jim Hester and Robert Krzyzanowski},
  year = {2021},
  note = {R package version 1.2.1},
  url = {https://github.com/kevinushey/rex},
}

@Manual{R-igraph,
  title = {{igraph}: Network Analysis and Visualization in R},
  author = {Gábor Csárdi and Tamás Nepusz and Vincent Traag and Szabolcs Horvát and Fabio Zanini and Daniel Noom and Kirill Müller},
  year = {2024},
  note = {R package version 2.0.3},
  doi = {10.5281/zenodo.7682609},
  url = {https://CRAN.R-project.org/package=igraph},
}

@Article{R-coda,
  title = {CODA: Convergence Diagnosis and Output Analysis for MCMC},
  author = {Martyn Plummer and Nicky Best and Kate Cowles and Karen Vines},
  journal = {R News},
  year = {2006},
  volume = {6},
  number = {1},
  pages = {7--11},
  url = {https://journal.r-project.org/archive/},
  pdf = {https://www.r-project.org/doc/Rnews/Rnews_2006-1.pdf},
}

@Article{R-texreg,
  title = {{texreg}: Conversion of Statistical Model Output in {R} to {\LaTeX} and {HTML} Tables},
  author = {Philip Leifeld},
  journal = {Journal of Statistical Software},
  year = {2013},
  volume = {55},
  number = {8},
  pages = {1--24},
  url = {https://doi.org/10.18637/jss.v055.i08},
}

@Manual{R-ergm,
  author = {Mark S. Handcock and David R. Hunter and Carter T. Butts and Steven M. Goodreau and Pavel N. Krivitsky and Martina Morris},
  title = {ergm: Fit, Simulate and Diagnose Exponential-Family Models for
Networks},
  organization = {The Statnet Project (\url{http://www.statnet.org})},
  year = {2018},
  note = {R package version 3.9.4},
  url = {https://CRAN.R-project.org/package=ergm},
}

@Manual{R-intergraph,
  title = {{intergraph}: Coercion Routines for Network Data Objects},
  author = {Michał Bojanowski},
  year = {2023},
  url = {https://mbojan.github.io/intergraph/},
  note = {R package version 2.0-3},
}

  @Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2024},
    url = {https://www.R-project.org/},
  }

@Manual{R-latticeExtra,
  title = {latticeExtra: Extra Graphical Utilities Based on Lattice},
  author = {Deepayan Sarkar and Felix Andrews},
  year = {2022},
  note = {R package version 0.6-30},
  url = {http://latticeextra.r-forge.r-project.org/},
}