skeleton.bib

@article{JSSv067i01,
   author = {Douglas Bates and Martin Mächler and Ben Bolker and Steve Walker},
   title = {Fitting Linear Mixed-Effects Models Using lme4},
   journal = {Journal of Statistical Software, Articles},
   volume = {67},
   number = {1},
   year = {2015},
   keywords = {sparse matrix methods; linear mixed models; penalized least squares; Cholesky decomposition},
   abstract = {Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.},
   issn = {1548-7660},
   pages = {1--48},
   doi = {10.18637/jss.v067.i01},
   url = {https://www.jstatsoft.org/v067/i01}
}

@article{RJ-2017-066,
  author = {Mollie E. Brooks and Kasper Kristensen and Koen J. van
          Benthem and Arni Magnusson and Casper W. Berg and Anders
          Nielsen and Hans J. Skaug and Martin Mächler and Benjamin M.
          Bolker},
  title = {{glmmTMB Balances Speed and Flexibility Among Packages for
          Zero-inflated Generalized Linear Mixed Modeling}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-066},
  url = {https://doi.org/10.32614/RJ-2017-066},
  pages = {378--400},
  volume = {9},
  number = {2}
}

@article{bolker2012owls,
  title={Owls example: a zero-inflated, generalized linear mixed model for count data},
  author={Bolker, Ben and Brooks, Mollie and Gardner, Beth and Lennert, Cleridy and Minami, Mihoko},
  journal={Departments of Mathematics \& Statistics and Biology, McMaster University, Hamilton},
  url = {https://groups.nceas.ucsb.edu/non-linear-modeling/projects/owls/WRITEUP/owls.pdf/@@download},
  year={2012}
}

@article{bolker2021faqs,
  title={GLMM FAQ},
  author={Bolker, Ben and others},
  journal={},
  url = {http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#zero-inflation},
  year={2021}
}

@book{zuur2009mixed,
  title={Mixed effects models and extensions in ecology with R},
  author={Zuur, Alain and Ieno, Elena N and Walker, Neil and Saveliev, Anatoly A and Smith, Graham M},
  year={2009},
  publisher={Springer Science \& Business Media}
}

@book{hilbe2011negative,
  title={Negative binomial regression},
  author={Hilbe, Joseph M},
  year={2011},
  doi = {10.1017/CBO9780511973420},
  url = {https://doi.org/10.1017/CBO9780511973420},
  publisher={Cambridge University Press}
}

@article{bolker2013strategies,
  title={Strategies for fitting nonlinear ecological models in R, AD M odel B uilder, and BUGS},
  author={Bolker, Benjamin M and Gardner, Beth and Maunder, Mark and Berg, Casper W and Brooks, Mollie and Comita, Liza and Crone, Elizabeth and Cubaynes, Sarah and Davies, Trevor and de Valpine, Perry and others},
  journal={Methods in Ecology and Evolution},
  volume={4},
  number={6},
  pages={501--512},
  year={2013},
  doi = {doi.org/10.1111/2041-210x.12044},
  url = {https://doi.org/10.1111/2041-210x.12044},
  publisher={Wiley Online Library}
}

@Manual{R-base,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2020},
  url = {https://www.R-project.org/},
}

@misc{bolker2020getting,
  title={Getting started with the glmmTMB package},
  author={Bolker, Ben},
  year={2020},
  publisher={R Foundation for Statistical Computing Vienna, Austria}
}

@Manual{R-rmarkdown,
  title = {rmarkdown: Dynamic Documents for R},
  author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
  year = {2020},
  note = {R package version 2.4},
  url = {https://github.com/rstudio/rmarkdown},
}

@Book{rmarkdown2018,
  title = {R Markdown: The Definitive Guide},
  author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2018},
  note = {ISBN 9781138359338},
  url = {https://bookdown.org/yihui/rmarkdown},
}

@Book{rmarkdown2020,
  title = {R Markdown Cookbook},
  author = {Yihui Xie and Christophe Dervieux and Emily Riederer},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2020},
  note = {ISBN 9780367563837},
  url = {https://bookdown.org/yihui/rmarkdown-cookbook},
}