bib.bib

@book{agrestiFoundationsLinearGeneralized2015,
  title = {Foundations of Linear and Generalized Linear Models},
  author = {Agresti, Alan},
  date = {2015-01-15},
  eprint = {dgIzBgAAQBAJ},
  eprinttype = {googlebooks},
  publisher = {{John Wiley \& Sons}},
  url = {https://www.wiley.com/en-us/Foundations+of+Linear+and+Generalized+Linear+Models-p-9781118730034},
  abstract = {A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features:  An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises  An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.},
  isbn = {978-1-118-73005-8},
  langid = {english},
  pagetotal = {469},
  keywords = {Mathematics / Probability & Statistics / General,Mathematics / Probability & Statistics / Stochastic Processes}
}

@article{barrRandomEffectsStructure2013,
  title = {Random Effects Structure for Confirmatory Hypothesis Testing: {{Keep}} It Maximal},
  shorttitle = {Random Effects Structure for Confirmatory Hypothesis Testing},
  author = {Barr, Dale J. and Levy, Roger and Scheepers, Christoph and Tily, Harry J.},
  date = {2013-04-01},
  journaltitle = {Journal of Memory and Language},
  shortjournal = {Journal of Memory and Language},
  volume = {68},
  number = {3},
  pages = {255--278},
  issn = {0749-596X},
  doi = {10.1016/j.jml.2012.11.001},
  url = {http://www.sciencedirect.com/science/article/pii/S0749596X12001180},
  urldate = {2020-07-27},
  abstract = {Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F1 and F2 tests, and in many cases, even worse than F1 alone. Maximal LMEMs should be the `gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.},
  langid = {english},
  keywords = {Generalization,Linear mixed-effects models,Monte Carlo simulation,Statistics},
  file = {/Users/solomonkurz/Zotero/storage/FHRVR92C/Barr et al. - 2013 - Random effects structure for confirmatory hypothes.pdf;/Users/solomonkurz/Zotero/storage/7SG2QQCA/S0749596X12001180.html}
}

@article{batesFittingLinearMixedeffects2015,
  title = {Fitting Linear Mixed-Effects Models Using {{lme4}}},
  author = {Bates, Douglas and M\"achler, Martin and Bolker, Ben and Walker, Steve},
  date = {2015},
  journaltitle = {Journal of Statistical Software},
  volume = {67},
  number = {1},
  pages = {1--48},
  doi = {10.18637/jss.v067.i01}
}

@article{betancourtBayesSparse2018,
  title = {Bayes Sparse Regression},
  author = {Betancourt, Michael},
  date = {2018-03},
  url = {https://betanalpha.github.io/assets/case_studies/bayes_sparse_regression.html},
  langid = {english}
}

@online{BetterBibTeXZotero2020,
  title = {Better {{BibTeX}} for Zotero},
  author = {Heyns, Emiliano},
  date = {2020},
  url = {https://retorque.re/zotero-better-bibtex/},
  urldate = {2020-05-19}
}

@online{BibTeX2020,
  title = {{{BibTeX}}},
  date = {2020},
  url = {http://www.bibtex.org/},
  urldate = {2020-05-19},
  file = {/Users/solomonkurz/Zotero/storage/PMDJYC3M/www.bibtex.org.html}
}

@article{bickelSexBiasGraduate1975,
  title = {Sex Bias in Graduate Admissions: {{Data}} from {{Berkeley}}},
  shorttitle = {Sex {{Bias}} in {{Graduate Admissions}}},
  author = {Bickel, P. J. and Hammel, E. A. and O'Connell, J. W.},
  date = {1975-02-07},
  journaltitle = {Science},
  volume = {187},
  number = {4175},
  eprint = {17835295},
  eprinttype = {pmid},
  pages = {398--404},
  publisher = {{American Association for the Advancement of Science}},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.187.4175.398},
  url = {https://pdfs.semanticscholar.org/b704/3d57d399bd28b2d3e84fb9d342a307472458.pdf},
  urldate = {2020-06-17},
  abstract = {Examination of aggregate data on graduate admissions to the University of California, Berkeley, for fall 1973 shows a clear but misleading pattern of bias against female applicants. Examination of the disaggregated data reveals few decision-making units that show statistically significant departures from expected frequencies of female admissions, and about as many units appear to favor women as to favor men. If the data are properly pooled, taking into account the autonomy of departmental decision making, thus correcting for the tendency of women to apply to graduate departments that are more difficult for applicants of either sex to enter, there is a small but statistically significant bias in favor of women. The graduate departments that are easier to enter tend to be those that require more mathematics in the undergraduate preparatory curriculum. The bias in the aggregated data stems not from any pattern of discrimination on the part of admissions committees, which seem quite fair on the whole, but apparently from prior screening at earlier levels of the educational system. Women are shunted by their socialization and education toward fields of graduate study that are generally more crowded, less productive of completed degrees, and less well funded, and that frequently offer poorer professional employment prospects.},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/XW4GACMB/398.html}
}

@article{blavaan2021,
  title = {Efficient {{Bayesian}} Structural Equation Modeling in {{Stan}}},
  author = {Merkle, Edgar C. and Fitzsimmons, Ellen and Uanhoro, James and Goodrich, Ben},
  date = {2021},
  journaltitle = {Journal of Statistical Software},
  volume = {100},
  number = {6},
  pages = {1--22},
  doi = {10.18637/jss.v100.i06}
}

@incollection{borgesjlJardinSenderosQue1941,
  title = {El Jardin de Senderos Que Se Bifurcan. {{Buenos Aires}}: {{Sur}}. {{Translated}} by {{D}}. {{A}}. {{Yates}} (1964)},
  booktitle = {Labyrinths: {{Selected Stories}} \& {{Other Writings}}},
  author = {{Borges, JL}},
  date = {1941},
  pages = {19--29},
  publisher = {{New Directions}},
  location = {{New York}}
}

@book{brms2020RM,
  title = {{{brms}} Reference Manual, {{Version}} 2.14.4},
  author = {B\"urkner, Paul-Christian},
  date = {2020},
  url = {https://CRAN.R-project.org/package=brms/brms.pdf}
}

@book{brms2022RM,
  title = {{{brms}} Reference Manual, {{Version}} 2.18.0},
  author = {B\"urkner, Paul-Christian},
  date = {2022},
  url = {https://CRAN.R-project.org/package=brms/brms.pdf}
}

@book{bryanHappyGitGitHub2020,
  title = {Happy {{Git}} and {{GitHub}} for the {{useR}}},
  author = {Bryan, Jenny and {the STAT 545 TAs} and Hester, Jim},
  date = {2020},
  url = {https://happygitwithr.com}
}

@article{Bürkner2022Define,
  title = {Define Custom Response Distributions with Brms},
  author = {B\"urkner, Paul-Christian},
  date = {2022-09-19},
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_customfamilies.html}
}

@article{Bürkner2022Distributional,
  title = {Estimating Distributional Models with Brms},
  author = {B\"urkner, Paul-Christian},
  date = {2022-04},
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_distreg.html}
}

@article{Bürkner2022HandleMissingValues,
  title = {Handle Missing Values with Brms},
  author = {B\"urkner, Paul-Christian},
  date = {2022-09-19},
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_missings.html},
  urldate = {2022-09-26}
}

@article{Bürkner2022Multivariate,
  title = {Estimating Multivariate Models with Brms},
  author = {B\"urkner, Paul-Christian},
  date = {2022-09-19},
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_multivariate.html},
  urldate = {2022-09-25}
}

@article{Bürkner2022Non_linear,
  title = {Estimating Non-Linear Models with Brms},
  author = {B\"urkner, Paul-Christian},
  date = {2022-09-19},
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_nonlinear.html}
}

@article{Bürkner2022Parameterization,
  title = {Parameterization of Response Distributions in Brms},
  author = {B\"urkner, Paul-Christian},
  date = {2022-09-19},
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_families.html},
  urldate = {2022-09-26}
}

@article{Bürkner2022Phylogenetic,
  title = {Estimating Phylogenetic Multilevel Models with Brms},
  author = {B\"urkner, Paul-Christian},
  date = {2022-09-19},
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_phylogenetics.html},
  urldate = {2022-09-26}
}

@article{burknerAdvancedBayesianMultilevel2018,
  title = {Advanced {{Bayesian}} Multilevel Modeling with the {{R}} Package Brms},
  author = {B\"urkner, Paul-Christian},
  date = {2018},
  journaltitle = {The R Journal},
  volume = {10},
  number = {1},
  pages = {395--411},
  doi = {10.32614/RJ-2018-017}
}

@article{burknerBrmsPackageBayesian2017,
  title = {{{brms}}: {{An R}} Package for {{Bayesian}} Multilevel Models Using {{Stan}}},
  author = {B\"urkner, Paul-Christian},
  date = {2017},
  journaltitle = {Journal of Statistical Software},
  volume = {80},
  number = {1},
  pages = {1--28},
  doi = {10.18637/jss.v080.i01}
}

@article{burknerOrdinalRegressionModels2019,
  title = {Ordinal Regression Models in Psychology: {{A}} Tutorial},
  shorttitle = {Ordinal {{Regression Models}} in {{Psychology}}},
  author = {B\"urkner, Paul-Christian and Vuorre, Matti},
  date = {2019-03-01},
  journaltitle = {Advances in Methods and Practices in Psychological Science},
  shortjournal = {Advances in Methods and Practices in Psychological Science},
  volume = {2},
  number = {1},
  pages = {77--101},
  publisher = {{SAGE Publications Inc}},
  issn = {2515-2459},
  doi = {10.1177/2515245918823199},
  url = {https://doi.org/10.1177/2515245918823199},
  urldate = {2020-05-18},
  abstract = {Ordinal variables, although extremely common in psychology, are almost exclusively analyzed with statistical models that falsely assume them to be metric. This practice can lead to distorted effect-size estimates, inflated error rates, and other problems. We argue for the application of ordinal models that make appropriate assumptions about the variables under study. In this Tutorial, we first explain the three major classes of ordinal models: the cumulative, sequential, and adjacent-category models. We then show how to fit ordinal models in a fully Bayesian framework with the R package brms, using data sets on opinions about stem-cell research and time courses of marriage. The appendices provide detailed mathematical derivations of the models and a discussion of censored ordinal models. Compared with metric models, ordinal models provide better theoretical interpretation and numerical inference from ordinal data, and we recommend their widespread adoption in psychology.},
  langid = {english}
}

@article{carpenterStanProbabilisticProgramming2017,
  title = {Stan: {{A}} Probabilistic Programming Language},
  author = {Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
  date = {2017},
  journaltitle = {Journal of statistical software},
  volume = {76},
  number = {1},
  publisher = {{Columbia Univ., New York, NY (United States); Harvard Univ., Cambridge, MA \ldots}},
  doi = {10.18637/jss.v076.i01},
  url = {https://www.osti.gov/servlets/purl/1430202}
}

@inproceedings{carvalho2009handling,
  title = {Handling Sparsity via the Horseshoe},
  booktitle = {Artificial Intelligence and Statistics},
  author = {Carvalho, Carlos M and Polson, Nicholas G and Scott, James G},
  date = {2009},
  pages = {73--80},
  url = {http://proceedings.mlr.press/v5/carvalho09a/carvalho09a.pdf}
}

@article{casellaExplainingGibbsSampler1992,
  title = {Explaining the {{Gibbs}} Sampler},
  author = {Casella, George and George, Edward I.},
  date = {1992-08-01},
  journaltitle = {The American Statistician},
  volume = {46},
  number = {3},
  pages = {167--174},
  publisher = {{Taylor \& Francis}},
  issn = {0003-1305},
  doi = {10.1080/00031305.1992.10475878},
  url = {https://ecommons.cornell.edu/bitstream/handle/1813/31670/BU-1098-MA.Revised.pdf?sequence=1},
  urldate = {2020-06-11},
  abstract = {Computer-intensive algorithms, such as the Gibbs sampler, have become increasingly popular statistical tools, both in applied and theoretical work. The properties of such algorithms, however, may sometimes not be obvious. Here we give a simple explanation of how and why the Gibbs sampler works. We analytically establish its properties in a simple case and provide insight for more complicated cases. There are also a number of examples.},
  keywords = {Data augmentation,Markov chains,Monte Carlo methods,Resampling techniques},
  annotation = {\_eprint: https://www.tandfonline.com/doi/pdf/10.1080/00031305.1992.10475878},
  file = {/Users/solomonkurz/Zotero/storage/7G3SEDKK/Casella and George - 1992 - Explaining the Gibbs Sampler.pdf;/Users/solomonkurz/Zotero/storage/SFZUD4XZ/00031305.1992.html}
}

@article{cushmanRoleConsciousReasoning2006,
  title = {The Role of Conscious Reasoning and Intuition in Moral Judgment: {{Testing}} Three Principles of Harm},
  shorttitle = {The {{Role}} of {{Conscious Reasoning}} and {{Intuition}} in {{Moral Judgment}}},
  author = {Cushman, Fiery and Young, Liane and Hauser, Marc},
  date = {2006-12-01},
  journaltitle = {Psychological Science},
  shortjournal = {Psychol Sci},
  volume = {17},
  number = {12},
  pages = {1082--1089},
  publisher = {{SAGE Publications Inc}},
  issn = {0956-7976},
  doi = {10.1111/j.1467-9280.2006.01834.x},
  url = {https://doi.org/10.1111/j.1467-9280.2006.01834.x},
  urldate = {2020-06-27},
  abstract = {Is moral judgment accomplished by intuition or conscious reasoning? An answer demands a detailed account of the moral principles in question. We investigated three principles that guide moral judgments: (a) Harm caused by action is worse than harm caused by omission, (b) harm intended as the means to a goal is worse than harm foreseen as the side effect of a goal, and (c) harm involving physical contact with the victim is worse than harm involving no physical contact. Asking whether these principles are invoked to explain moral judgments, we found that subjects generally appealed to the first and third principles in their justifications, but not to the second. This finding has significance for methods and theories of moral psychology: The moral principles used in judgment must be directly compared with those articulated in justification, and doing so shows that some moral principles are available to conscious reasoning whereas others are not.},
  langid = {english}
}

@article{efronSteinParadoxStatistics1977,
  title = {Stein's Paradox in Statistics},
  author = {Efron, Bradley and Morris, Carl},
  date = {1977},
  journaltitle = {Scientific American},
  volume = {236},
  number = {5},
  eprint = {24954030},
  eprinttype = {jstor},
  pages = {119--127},
  publisher = {{Scientific American, a division of Nature America, Inc.}},
  issn = {0036-8733},
  doi = {10.1038/scientificamerican0577-119}
}

@book{enders2010applied,
  title = {Applied Missing Data Analysis},
  author = {Enders, Craig K},
  date = {2010},
  publisher = {{Guilford Press}},
  url = {http://www.appliedmissingdata.com/},
  isbn = {978-1-60623-639-0}
}

@book{enders2022applied,
  title = {Applied Missing Data Analysis},
  author = {Enders, Craig K},
  date = {2022},
  edition = {Second Edition},
  publisher = {{Guilford Press}},
  url = {http://www.appliedmissingdata.com/},
  isbn = {978-1-4625-4986-3}
}

@article{fernandezGGMCMCAnalysisofMCMC2016,
  title = {{{ggmcmc}}: {{Analysis}} of {{MCMC}} Samples and {{Bayesian}} Inference},
  author = {Fern\'andez i Mar\'in, Xavier},
  date = {2016},
  journaltitle = {Journal of Statistical Software},
  volume = {70},
  number = {9},
  pages = {1--20},
  doi = {10.18637/jss.v070.i09}
}

@article{gabry2019visualization,
  title = {Visualization in {{Bayesian}} Workflow},
  author = {Gabry, Jonah and Simpson, Daniel and Vehtari, Aki and Betancourt, Michael and Gelman, Andrew},
  date = {2019},
  journaltitle = {Journal of the Royal Statistical Society: Series A (Statistics in Society)},
  volume = {182},
  number = {2},
  pages = {389--402},
  publisher = {{Wiley Online Library}},
  doi = {10.1111/rssa.12378},
  url = {https://arxiv.org/abs/1709.01449}
}

@article{gabryPlottingMCMCDraws2019,
  title = {Plotting {{MCMC}} Draws Using the Bayesplot Package},
  author = {Gabry, Jonah},
  date = {2020-05-27},
  url = {https://CRAN.R-project.org/package=bayesplot/vignettes/plotting-mcmc-draws.html},
  urldate = {2020-05-26},
  langid = {english}
}

@article{gabryPlottingMCMCDraws2022,
  title = {Plotting {{MCMC}} Draws Using the Bayesplot Package},
  author = {Gabry, Jonah},
  date = {2022-11-16},
  url = {https://CRAN.R-project.org/package=bayesplot/vignettes/plotting-mcmc-draws.html},
  urldate = {2022-09-25},
  langid = {english}
}

@article{gabryVisualMCMCDiagnostics2020,
  title = {Visual {{MCMC}} Diagnostics Using the Bayesplot Package},
  author = {Gabry, Jonah and Modr\'ak, Martin},
  date = {2020-05-27},
  url = {https://CRAN.R-project.org/package=bayesplot/vignettes/visual-mcmc-diagnostics.html},
  urldate = {2020-06-11},
  langid = {english}
}

@article{gabryVisualMCMCDiagnostics2022,
  title = {Visual {{MCMC}} Diagnostics Using the {{bayesplot}} Package},
  author = {Gabry, Jonah and Modr\'ak, Martin},
  date = {2022-11-16},
  url = {https://CRAN.R-project.org/package=bayesplot/vignettes/plotting-mcmc-draws.html},
  urldate = {2022-05-15},
  langid = {english}
}

@book{gelman2013bayesian,
  title = {Bayesian Data Analysis},
  author = {Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B},
  date = {2013},
  edition = {Third Edition},
  publisher = {{CRC press}},
  url = {https://stat.columbia.edu/~gelman/book/}
}

@article{gelmanGardenForkingPaths2013,
  title = {The Garden of Forking Paths: {{Why}} Multiple Comparisons Can Be a Problem, Even When There Is No ``Fishing Expedition'' or ``p-Hacking'' and the Research Hypothesis Was Posited Ahead of Time},
  author = {Gelman, Andrew and Loken, Eric},
  date = {2013-11-14},
  pages = {17},
  url = {https://stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf},
  abstract = {Researcher degrees of freedom can lead to a multiple comparisons problem, even in settings where researchers perform only a single analysis on their data. The problem is there can be a large number of potential comparisons when the details of data analysis are highly contingent on data, without the researcher having to perform any conscious procedure of fishing or examining multiple p-values. We discuss in the context of several examples of published papers where data-analysis decisions were theoretically-motivated based on previous literature, but where the details of data selection and analysis were not pre-specified and, as a result, were contingent on data.},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/EA32DKC7/Gelman and Loken - The garden of forking paths Why multiple comparis.pdf}
}

@article{gelmanPriorCanOften2017,
  title = {The Prior Can Often Only Be Understood in the Context of the Likelihood},
  author = {Gelman, Andrew and Simpson, Daniel and Betancourt, Michael},
  date = {2017-10},
  journaltitle = {Entropy},
  volume = {19},
  number = {10},
  pages = {555},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  doi = {10.3390/e19100555},
  url = {https://www.mdpi.com/1099-4300/19/10/555},
  urldate = {2020-06-12},
  abstract = {A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys' priors, reference priors, maximum entropy priors, and weakly informative priors. These methods, however, often manifest a key conceptual tension in prior modeling: a model encoding true prior information should be chosen without reference to the model of the measurement process, but almost all common prior modeling techniques are implicitly motivated by a reference likelihood. In this paper we resolve this apparent paradox by placing the choice of prior into the context of the entire Bayesian analysis, from inference to prediction to model evaluation.},
  issue = {10},
  langid = {english},
  keywords = {Bayesian inference,default priors,prior distribution},
  file = {/Users/solomonkurz/Zotero/storage/GITEJRKC/Gelman et al. - 2017 - The Prior Can Often Only Be Understood in the Cont.pdf;/Users/solomonkurz/Zotero/storage/FD2UD59C/555.html}
}

@article{gelmanRsquaredBayesianRegression2019,
  title = {R-Squared for {{Bayesian}} Regression Models},
  author = {Gelman, Andrew and Goodrich, Ben and Gabry, Jonah and Vehtari, Aki},
  date = {2019-07-03},
  journaltitle = {The American Statistician},
  shortjournal = {The American Statistician},
  volume = {73},
  number = {3},
  pages = {307--309},
  issn = {0003-1305, 1537-2731},
  doi = {10.1080/00031305.2018.1549100},
  url = {https://www.tandfonline.com/doi/full/10.1080/00031305.2018.1549100},
  urldate = {2020-05-16},
  langid = {english}
}

@article{gemanStochasticRelaxationGibbs1984,
  title = {Stochastic Relaxation, {{Gibbs}} Distributions, and the {{Bayesian}} Restoration of Images},
  author = {Geman, Stuart and Geman, Donald},
  date = {1984-11},
  journaltitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {PAMI-6},
  number = {6},
  pages = {721--741},
  issn = {1939-3539},
  doi = {10.1109/TPAMI.1984.4767596},
  url = {https://www.dam.brown.edu/people/documents/stochasticrelaxation.pdf},
  abstract = {We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs distribution, Markov random field (MRF) equivalence, this assignment also determines an MRF image model. The energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the MRF. For a range of degradation mechanisms, including blurring, nonlinear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical system isolates low energy states (``annealing''), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result is a highly parallel ``relaxation'' algorithm for MAP estimation. We establish convergence properties of the algorithm and we experiment with some simple pictures, for which good restorations are obtained at low signal-to-noise ratios.},
  eventtitle = {{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}},
  keywords = {Additive noise,Annealing,Bayesian methods,Deformable models,Degradation,Energy states,Gibbs distribution,image restoration,Image restoration,line process,MAP estimate,Markov random field,Markov random fields,relaxation,scene modeling,spatial degradation,Stochastic processes,Temperature distribution},
  file = {/Users/solomonkurz/Zotero/storage/M4USX4TH/4767596.html}
}

@article{gershoff2016spanking,
  title = {Spanking and Child Outcomes: {{Old}} Controversies and New Meta-Analyses.},
  author = {Gershoff, Elizabeth T and Grogan-Kaylor, Andrew},
  date = {2016},
  journaltitle = {Journal of family psychology},
  volume = {30},
  number = {4},
  pages = {453},
  publisher = {{American Psychological Association}},
  doi = {10.1037/fam0000191},
  url = {https://pdfs.semanticscholar.org/0d03/a2e9f085f0a268b4c0a52f5ac31c17a3e5f3.pdf}
}

@article{gershoffSpankingChildDevelopment2013,
  title = {Spanking and Child Development: {{We}} Know Enough Now to Stop Hitting Our Children},
  shorttitle = {Spanking and {{Child Development}}},
  author = {Gershoff, Elizabeth T.},
  date = {2013},
  journaltitle = {Child Development Perspectives},
  volume = {7},
  number = {3},
  pages = {133--137},
  issn = {1750-8606},
  doi = {10.1111/cdep.12038},
  url = {https://srcd.onlinelibrary.wiley.com/doi/abs/10.1111/cdep.12038},
  urldate = {2020-10-10},
  abstract = {Spanking remains a common, if controversial, childrearing practice in the United States. In this article, I pair mounting research indicating that spanking is both ineffective and harmful with professional and human rights opinions disavowing the practice. I conclude that spanking is a form of violence against children that should no longer be a part of American childrearing.},
  langid = {english},
  keywords = {corporal punishment,spanking,violence against children},
  annotation = {\_eprint: https://srcd.onlinelibrary.wiley.com/doi/pdf/10.1111/cdep.12038},
  file = {/Users/solomonkurz/Zotero/storage/5PJHB2U6/Gershoff - 2013 - Spanking and Child Development We Know Enough Now.pdf;/Users/solomonkurz/Zotero/storage/6NA8CX3K/cdep.html}
}

@article{gershoffSpankingChildOutcomes2016,
  title = {Spanking and Child Outcomes: {{Old}} Controversies and New Meta-Analyses.},
  shorttitle = {Spanking and Child Outcomes},
  author = {Gershoff, Elizabeth T. and Grogan-Kaylor, Andrew},
  date = {2016},
  journaltitle = {Journal of Family Psychology},
  shortjournal = {Journal of Family Psychology},
  volume = {30},
  number = {4},
  pages = {453--469},
  issn = {1939-1293, 0893-3200},
  doi = {10.1037/fam0000191},
  url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/fam0000191},
  urldate = {2020-10-10},
  abstract = {This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Whether spanking is helpful or harmful to children continues to be the source of considerable debate among both researchers and the public. This article addresses 2 persistent issues, namely whether effect sizes for spanking are distinct from those for physical abuse, and whether effect sizes for spanking are robust to study design differences. Meta-analyses focused specifically on spanking were conducted on a total of 111 unique effect sizes representing 160,927 children. Thirteen of 17 mean effect sizes were significantly different from zero and all indicated a link between spanking and increased risk for detrimental child outcomes. Effect sizes did not substantially differ between spanking and physical abuse or by study design characteristics.},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/MS8BWHT8/Gershoff and Grogan-Kaylor - 2016 - Spanking and child outcomes Old controversies and.pdf}
}

@book{grafenModernStatisticsLife2002,
  title = {Modern Statistics for the Life Sciences},
  author = {Grafen, Alan and Hails, Rosie},
  date = {2002-05-09},
  publisher = {{Oxford University Press}},
  location = {{Oxford, New York}},
  url = {https://global.oup.com/academic/product/modern-statistics-for-the-life-sciences-9780199252312?},
  abstract = {Model formulae represent a powerful methodology for describing, discussing, understanding, and performing the component of statistical tests known as linear statistics. It was developed for professional statisticians in the 1960s and has become increasingly available as the use of computers has grown and software has advanced. Modern Statistics for Life Scientists puts this methodology firmly within the grasp of undergraduates for the first time. The authors assume a basic knowledge of statistics--up to and including one and two sample t-tests and their non-parametric equivalents. They provide the conceptual framework needed to understand what the method does--but without mathematical proofs--and introduce the ideas in a simple and steady progression with worked examples and exercises at every stage.  This innovative text offers students a single conceptual framework for a wide range of tests-including t-tests, oneway and multiway analysis of variance, linear and polynomial regressions, and analysis of covariance-that are usually introduced separately. More importantly, it gives students a language in which they can frame questions and communicate with the computers that perform the analyses. A companion website, www.oup.com/grafenhails, provides a wealth of worked exercises in the three statistical languages; Minitab, SAS, and SPSS. Appropriate for use in statistics courses at undergraduate and graduate levels, Modern Statistics for the Life Sciences  is also a helpful resource for students in non-mathematics-based disciplines using statistics, such as geography, psychology, epidemiology, and ecology.},
  isbn = {978-0-19-925231-2},
  pagetotal = {368},
  file = {/Users/solomonkurz/Zotero/storage/9CLZ5C5J/modern-statistics-for-the-life-sciences-9780199252312.html}
}

@book{grolemundDataScience2017,
  title = {R for Data Science},
  author = {Grolemund, Garrett and Wickham, Hadley},
  date = {2017},
  publisher = {{O'Reilly}},
  url = {https://r4ds.had.co.nz}
}

@article{hauerHarmDoneTests2004,
  title = {The Harm Done by Tests of Significance},
  author = {Hauer, Ezra},
  date = {2004-05},
  journaltitle = {Accident Analysis \& Prevention},
  shortjournal = {Accident Analysis \& Prevention},
  volume = {36},
  number = {3},
  pages = {495--500},
  issn = {00014575},
  doi = {10.1016/S0001-4575(03)00036-8},
  url = {https://statmodeling.stat.columbia.edu/wp-content/uploads/2013/03/1154-Hauer-The-harm-done-by-tests-of-significance.pdf},
  urldate = {2020-05-21},
  abstract = {Three historical episodes in which the application of null hypothesis significance testing (NHST) led to the mis-interpretation of data are described. It is argued that the pervasive use of this statistical ritual impedes the accumulation of knowledge and is unfit for use.},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/Y8LYGNMT/Hauer - 2004 - The harm done by tests of significance.pdf}
}

@book{healyDataVisualization2018,
  title = {Data Visualization: {{A}} Practical Introduction},
  author = {Healy, Kieran},
  date = {2018},
  publisher = {{Princeton University Press}},
  url = {https://socviz.co/},
  isbn = {978-0-691-18161-5}
}

@article{hindePrimateMilkProximate2011,
  title = {Primate Milk: {{Proximate}} Mechanisms and Ultimate Perspectives},
  shorttitle = {Primate Milk},
  author = {Hinde, Katie and Milligan, Lauren A.},
  date = {2011},
  journaltitle = {Evolutionary Anthropology: Issues, News, and Reviews},
  volume = {20},
  number = {1},
  pages = {9--23},
  issn = {1520-6505},
  doi = {10.1002/evan.20289},
  url = {https://www.researchgate.net/publication/51751742_Primate_milk_Proximate_mechanisms_and_ultimate_perspectives},
  urldate = {2020-05-26},
  abstract = {To understand the evolutionary forces that have shaped primate lactation strategies, it is important to understand the proximate mechanisms of milk synthesis and their ecological and phylogenetic contexts. The lactation strategy of a species has four interrelated dimensions: the frequency and duration of nursing bouts, the period of lactation until weaning, the number and sex ratio of infants that a mother rears simultaneously, and the composition and yield of the milk that mothers synthesize. Milk synthesis, arguably the most physiologically costly component of rearing infants, remains the least studied. Energy transfer becomes energetically less efficient, transitioning from placental support to milk synthesis1, 2 just as the energy requirements for infant growth, development, and behavioral activity substantially increase. Here we review primate lactation biology and milk synthesis, integrating studies from anthropology, biology, nutrition, animal science, immunology, and biochemistry, to identify the derived and ancestral features of primate milks and enhance our understanding of primate life history.},
  langid = {english},
  keywords = {infant development,lactation,life history,maternal investment,reproductive ecology},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/evan.20289},
  file = {/Users/solomonkurz/Zotero/storage/7YIU3Z5X/evan.html}
}

@online{HomeTwitter,
  title = {Home / {{Twitter}}},
  url = {https://twitter.com/home},
  urldate = {2020-10-07},
  langid = {english},
  organization = {{Twitter}},
  file = {/Users/solomonkurz/Zotero/storage/FA8HZQUL/home.html}
}

@book{howell2001demography,
  title = {Demography of the Dobe! {{Kung}}},
  author = {Howell, Nancy},
  date = {2001},
  edition = {2nd Edition},
  publisher = {{Routledge}},
  url = {https://www.routledge.com/Demography-of-the-Dobe-Kung/Howell/p/book/9780202306490},
  isbn = {978-0-202-30649-0}
}

@book{howell2010life,
  title = {Life Histories of the {{Dobe}}! {{Kung}}: Food, Fatness, and Well-Being over the Life Span},
  author = {Howell, Nancy},
  date = {2010},
  volume = {4},
  publisher = {{Univ of California Press}},
  url = {https://www.ucpress.edu/book/9780520262348/life-histories-of-the-dobe-kung},
  isbn = {978-0-520-26234-8}
}

@misc{kallioinen2021DetectingAndDiagnosing,
  title = {Detecting and Diagnosing Prior and Likelihood Sensitivity with Power-Scaling},
  author = {Kallioinen, Noa and Paananen, Topi and B\"urkner, Paul-Christian and Vehtari, Aki},
  date = {2021},
  publisher = {{arXiv}},
  doi = {10.48550/ARXIV.2107.14054},
  url = {https://arxiv.org/abs/2107.14054},
  copyright = {arXiv.org perpetual, non-exclusive license},
  keywords = {FOS: Computer and information sciences,Methodology (stat.ME)}
}

@online{kayExtractingVisualizingTidy2020,
  title = {Extracting and Visualizing Tidy Draws from Brms Models},
  author = {Kay, Matthew},
  date = {2020-06-17},
  url = {https://mjskay.github.io/tidybayes/articles/tidy-brms.html},
  urldate = {2020-05-17},
  abstract = {tidybayes},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/NT83AM3T/tidy-brms.html}
}

@misc{kayExtractingVisualizingTidy2021,
  title = {Extracting and Visualizing Tidy Draws from Brms Models},
  author = {Kay, Matthew},
  date = {2021-12},
  url = {https://mjskay.github.io/tidybayes/articles/tidy-brms.html},
  urldate = {2022-04-15},
  abstract = {tidybayes}
}

@online{kayMarginalDistributionSingle2020,
  title = {Marginal Distribution of a Single Correlation from an {{LKJ}} Distribution},
  author = {Kay, Matthew},
  date = {2020},
  url = {https://mjskay.github.io/ggdist/reference/lkjcorr_marginal.html},
  urldate = {2020-07-29},
  abstract = {Marginal distribution for the correlation in a single cell from a correlation matrix distributed according to an LKJ distribution.},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/2TMG4ARE/lkjcorr_marginal.html}
}

@article{kelley2012effect,
  title = {On Effect Size},
  author = {Kelley, Ken and Preacher, Kristopher J},
  date = {2012},
  journaltitle = {Psychological methods},
  volume = {17},
  number = {2},
  pages = {137},
  publisher = {{American Psychological Association}},
  doi = {10.1037/a0028086},
  url = {https://www3.nd.edu/~kkelley/publications/articles/Kelley_and_Preacher_Psychological_Methods_2012.pdf}
}

@article{kievitSimpsonParadoxPsychological2013,
  title = {Simpson's Paradox in Psychological Science: A Practical Guide},
  shorttitle = {Simpson's Paradox in Psychological Science},
  author = {Kievit, Rogier and Frankenhuis, Willem Eduard and Waldorp, Lourens and Borsboom, Denny},
  date = {2013},
  journaltitle = {Frontiers in Psychology},
  shortjournal = {Front. Psychol.},
  volume = {4},
  publisher = {{Frontiers}},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2013.00513},
  url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00513/full},
  urldate = {2020-06-17},
  abstract = {The direction of an association at the population-level may be reversed within the subgroups comprising that population\textemdash a striking observation called Simpson's paradox. When facing this pattern, psychologists often view it as anomalous. Here, we argue that Simpson's paradox is more common than conventionally thought, and typically results in incorrect interpretations \textendash{} potentially with harmful consequences. We support this claim by drawing on empirical results from cognitive neuroscience, behavior genetics, psychopathology, personality psychology, educational psychology, intelligence research, and simulation studies. We show that Simpson's Paradox is most likely to occur when inferences are drawn across different levels of explanation (e.g., from populations to subgroups, or subgroups to individuals). We propose a set of statistical markers indicative of the paradox, and offer psychometric solutions for dealing with the paradox when encountered\textemdash including a toolbox in R for detecting Simpson's Paradox. We show that explicit modeling of situations in which the paradox might occur not only prevents incorrect interpretations of data, but also results in a deeper understanding of what data tell us about the world.},
  langid = {english},
  keywords = {ecological fallacy,Measurement,Paradox,Reductionism,simpson's paradox,statistical inference},
  file = {/Users/solomonkurz/Zotero/storage/2DI5JTLT/Kievit et al. - 2013 - Simpson's paradox in psychological science a prac.pdf}
}

@article{klinePopulationSizePredicts2010,
  title = {Population Size Predicts Technological Complexity in {{Oceania}}},
  author = {Kline, Michelle A. and Boyd, Robert},
  date = {2010-08-22},
  journaltitle = {Proceedings of the Royal Society B: Biological Sciences},
  shortjournal = {Proceedings of the Royal Society B: Biological Sciences},
  volume = {277},
  number = {1693},
  pages = {2559--2564},
  publisher = {{Royal Society}},
  doi = {10.1098/rspb.2010.0452},
  url = {https://royalsocietypublishing.org/doi/full/10.1098/rspb.2010.0452},
  urldate = {2020-06-17},
  abstract = {Much human adaptation depends on the gradual accumulation of culturally transmitted knowledge and technology. Recent models of this process predict that large, well-connected populations will have more diverse and complex tool kits than small, isolated populations. While several examples of the loss of technology in small populations are consistent with this prediction, it found no support in two systematic quantitative tests. Both studies were based on data from continental populations in which contact rates were not available, and therefore these studies do not provide a test of the models. Here, we show that in Oceania, around the time of early European contact, islands with small populations had less complicated marine foraging technology. This finding suggests that explanations of existing cultural variation based on optimality models alone are incomplete because demography plays an important role in generating cumulative cultural adaptation. It also indicates that hominin populations with similar cognitive abilities may leave very different archaeological records, a conclusion that has important implications for our understanding of the origin of anatomically modern humans and their evolved psychology.},
  file = {/Users/solomonkurz/Zotero/storage/674ZLAYD/Kline and Boyd - 2010 - Population size predicts technological complexity .pdf;/Users/solomonkurz/Zotero/storage/XYKLTPSX/rspb.2010.html}
}

@book{kruschkeDoingBayesianData2015,
  title = {Doing {{Bayesian}} Data Analysis: {{A}} Tutorial with {{R}}, {{JAGS}}, and {{Stan}}},
  author = {Kruschke, John K.},
  date = {2015},
  publisher = {{Academic Press}},
  url = {https://sites.google.com/site/doingbayesiandataanalysis/}
}

@article{kullbackInformationSufficiency1951,
  title = {On Information and Sufficiency},
  author = {Kullback, S. and Leibler, R. A.},
  date = {1951-03},
  journaltitle = {Annals of Mathematical Statistics},
  shortjournal = {Ann. Math. Statist.},
  volume = {22},
  number = {1},
  pages = {79--86},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0003-4851, 2168-8990},
  doi = {10.1214/aoms/1177729694},
  url = {https://projecteuclid.org/euclid.aoms/1177729694},
  urldate = {2020-06-01},
  abstract = {Project Euclid - mathematics and statistics online},
  langid = {english},
  mrnumber = {MR39968},
  zmnumber = {0042.38403},
  file = {/Users/solomonkurz/Zotero/storage/ZRSE9FMG/Kullback and Leibler - 1951 - On Information and Sufficiency.pdf;/Users/solomonkurz/Zotero/storage/QVXJE6S9/1177729694.html}
}

@book{kurzDoingBayesianData2020,
  title = {Doing {{Bayesian}} Data Analysis in Brms and the Tidyverse},
  author = {Kurz, A. Solomon},
  date = {2020-05-19},
  edition = {version 0.3.0},
  url = {https://bookdown.org/content/3686/},
  urldate = {2020-05-22},
  abstract = {This project is an attempt to re-express the code in Kruschke's (2015) textbook. His models are re-fit in brms, plots are redone with ggplot2, and the general data wrangling code predominantly follows the tidyverse style.},
  file = {/Users/solomonkurz/Zotero/storage/UKHWZ73Z/3686.html}
}

@book{kurzDoingBayesianDataAnalysis2022,
  title = {Doing {{Bayesian}} Data Analysis in Brms and the Tidyverse},
  author = {Kurz, A. Solomon},
  date = {2022-05},
  edition = {Version 1.0.0},
  url = {https://bookdown.org/content/3686/}
}

@book{kurzDoingBayesianDataAnalysis2023,
  title = {Doing {{Bayesian}} Data Analysis in Brms and the Tidyverse},
  author = {Kurz, A. Solomon},
  date = {2023-01},
  edition = {Version 1.1.0},
  url = {https://bookdown.org/content/3686/}
}

@book{kurzStatisticalRethinkingSecondEd2023,
  title = {Statistical {{Rethinking}} with Brms, {{ggplot2}}, and the Tidyverse: {{Second Edition}}},
  author = {Kurz, A. Solomon},
  date = {2023-01},
  edition = {version 0.4.0},
  url = {https://bookdown.org/content/4857/}
}

@book{leglerBroadeningYourStatistical2019,
  title = {Broadening Your Statistical Horizons: {{Generalized}} Linear Models and Multilevel Models},
  author = {Legler, Julie and Roback, Paul},
  date = {2019},
  url = {https://bookdown.org/roback/bookdown-bysh/}
}

@book{loo2020RM,
  title = {{{loo}} Reference Manual, {{Version}} 2.4.1},
  author = {Gabry, Jonah},
  date = {2020},
  url = {https://CRAN.R-project.org/package=loo/loo.pdf}
}

@book{loo2022RM,
  title = {{{loo}} Reference Manual, {{Version}} 2.5.1},
  author = {Gabry, Jonah},
  date = {2022-03-23},
  url = {https://CRAN.R-project.org/package=loo/loo.pdf}
}

@book{MASS2002,
  title = {Modern Applied Statistics with {{S}}},
  author = {Venables, W. N. and Ripley, B. D.},
  date = {2002},
  edition = {Fourth Edition},
  publisher = {{Springer}},
  location = {{New York}},
  url = {http://www.stats.ox.ac.uk/pub/MASS4}
}

@article{matejkaSameStats2017,
  title = {Same Stats, Different Graphs: {{Generating}} Datasets with Varied Appearance and Identical Statistics through Simulated Annealing},
  author = {Matejka, Justin and Fitzmaurice, George},
  date = {2017},
  url = {https://www.autodesk.com/research/publications/same-stats-different-graphs},
  urldate = {2020-10-10},
  langid = {english}
}

@software{matloffMatloffTidyverseSkeptic2020,
  title = {Matloff/{{TidyverseSkeptic}}},
  author = {Matloff, Norm},
  date = {2020-09-30T18:30:03Z},
  origdate = {2019-06-20T04:03:38Z},
  url = {https://github.com/matloff/TidyverseSkeptic},
  urldate = {2020-10-03},
  abstract = {An opinionated view of the Tidyverse "dialect" of the R language.}
}

@book{mcelreathStatisticalRethinkingBayesian2015,
  title = {Statistical Rethinking: {{A Bayesian}} Course with Examples in {{R}} and {{Stan}}},
  author = {McElreath, Richard},
  date = {2015},
  publisher = {{CRC press}},
  url = {https://xcelab.net/rm/statistical-rethinking/}
}

@book{mcelreathStatisticalRethinkingBayesian2020,
  title = {Statistical Rethinking: {{A Bayesian}} Course with Examples in {{R}} and {{Stan}}},
  shorttitle = {Statistical {{Rethinking}}},
  author = {McElreath, Richard},
  date = {2020-03-13},
  edition = {Second Edition},
  publisher = {{CRC Press}},
  url = {https://xcelab.net/rm/statistical-rethinking/},
  abstract = {Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.  The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.  The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.  Features   Integrates working code into the main text   Illustrates concepts through worked data analysis examples   Emphasizes understanding assumptions and how assumptions are reflected in code   Offers more detailed explanations of the mathematics in optional sections   Presents examples of using the dagitty R package to analyze causal graphs   Provides the rethinking R package on the author's website and on GitHub},
  isbn = {978-0-429-63914-2},
  langid = {english},
  pagetotal = {575},
  keywords = {Mathematics / Probability & Statistics / General}
}

@article{mchenryAustralopithecusHomoTransformations2000,
  title = {Australopithecus to {{Homo}}: {{Transformations}} in Body and Mind},
  shorttitle = {Australopithecus to {{Homo}}},
  author = {McHenry, Henry M. and Coffing, Katherine},
  date = {2000},
  journaltitle = {Annual Review of Anthropology},
  volume = {29},
  number = {1},
  pages = {125--146},
  doi = {10.1146/annurev.anthro.29.1.125},
  url = {https://www.researchgate.net/profile/Henry_Mchenry/publication/228368549_Australopithecus_To_Homo_Transformations_in_Body_and_Mind/links/572a6e1f08ae2efbfdbc20b6/Australopithecus-To-Homo-Transformations-in-Body-and-Mind.pdf},
  urldate = {2020-10-05},
  abstract = {Significant changes occurred in human evolution between 2.5 and 1.8 million years ago. Stone tools first appeared, brains expanded, bodies enlarged, sexual dimorphism in body size decreased, limb proportions changed, cheek teeth reduced in size, and crania began to share more unique features with later Homo. Although the two earliest species of Homo, H. habilis and H. rudolfensis, retained many primitive features in common with australopithecine species, they both shared key unique features with later species of Homo. Two of the most conspicuous shared derived characters were the sizes of the brain and masticatory apparatus relative to body weight. Despite the shared derived characters of H. habilis and H. rudolfensis, one unexpected complication in the transition from australopithecine to Homo was that the postcranial anatomy of H. habilis retained many australopithecine characteristics. H. rudolfensis, however, seems to have had a more human-like body plan, similar to later species of Homo. H. rudolfensis may therefore represent a link between Australopithecus and Homo.},
  annotation = {\_eprint: https://doi.org/10.1146/annurev.anthro.29.1.125}
}

@article{Merkle2018blavaan,
  title = {{{blavaan}}: {{Bayesian}} Structural Equation Models via Parameter Expansion},
  author = {Merkle, Edgar C. and Rosseel, Yves},
  date = {2018},
  journaltitle = {Journal of Statistical Software},
  volume = {85},
  number = {4},
  pages = {1--30},
  doi = {10.18637/jss.v085.i04}
}

@article{navarroDevilDeepBlue2019,
  title = {Between the Devil and the Deep Blue Sea: {{Tensions}} between Scientific Judgement and Statistical Model Selection},
  shorttitle = {Between the {{Devil}} and the {{Deep Blue Sea}}},
  author = {Navarro, Danielle J.},
  date = {2019-03},
  journaltitle = {Computational Brain \& Behavior},
  shortjournal = {Comput Brain Behav},
  volume = {2},
  number = {1},
  pages = {28--34},
  issn = {2522-0861, 2522-087X},
  doi = {10.1007/s42113-018-0019-z},
  url = {http://link.springer.com/10.1007/s42113-018-0019-z},
  urldate = {2020-05-15},
  abstract = {Discussions of model selection in the psychological literature typically frame the issues as a question of statistical inference, with the goal being to determine which model makes the best predictions about data. Within this setting, advocates of leaveone-out cross-validation and Bayes factors disagree on precisely which prediction problem model selection questions should aim to answer. In this comment, I discuss some of these issues from a scientific perspective. What goal does model selection serve when all models are known to be systematically wrong? How might ``toy problems'' tell a misleading story? How does the scientific goal of explanation align with (or differ from) traditional statistical concerns? I do not offer answers to these questions, but hope to highlight the reasons why psychological researchers cannot avoid asking them.},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/3D6FMZVD/Navarro - 2019 - Between the Devil and the Deep Blue Sea Tensions .pdf}
}

@book{navarroLearningStatistics2019,
  title = {Learning Statistics with {{R}}},
  author = {Navarro, Danielle},
  date = {2019},
  url = {https://learningstatisticswithr.com},
  langid = {english}
}

@book{nicenboim2022introduction,
  title = {An Introduction to {{Bayesian}} Data Analysis for Cognitive Science},
  author = {Nicenboim, Bruno and Schad, Daniel and Vasishth, Shravan},
  date = {2022-09-12},
  url = {https://vasishth.github.io/bayescogsci/book/}
}

@article{nunn2012ruggedness,
  title = {Ruggedness: {{The}} Blessing of Bad Geography in {{Africa}}},
  author = {Nunn, Nathan and Puga, Diego},
  date = {2012},
  journaltitle = {Review of Economics and Statistics},
  volume = {94},
  number = {1},
  pages = {20--36},
  publisher = {{MIT Press}},
  doi = {10.1162/REST_a_00161},
  url = {https://scholar.harvard.edu/files/nunn/files/ruggedness.pdf}
}

@book{pengMasteringSoftwareDevelopment2017,
  title = {Mastering Software Development in \{\vphantom\}{{R}}\vphantom\{\}},
  author = {Peng, Roger D. and Kross, Sean and Anderson, Brooke},
  date = {2017-09-21},
  url = {https://github.com/rdpeng/RProgDA},
  urldate = {2020-07-29},
  abstract = {The book covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.},
  file = {/Users/solomonkurz/Zotero/storage/97WUVMRH/RProgDA.html}
}

@book{pengProgrammingDataScience2019,
  title = {R Programming for Data Science},
  author = {Peng, Roger D.},
  date = {2019},
  url = {https://bookdown.org/rdpeng/rprogdatascience/}
}

@book{pengProgrammingDataScience2020,
  title = {R Programming for Data Science},
  author = {Peng, Roger D.},
  date = {2020},
  url = {https://bookdown.org/rdpeng/rprogdatascience/}
}

@book{pengProgrammingDataScience2022,
  title = {R Programming for Data Science},
  author = {Peng, Roger D.},
  date = {2022-05-31},
  url = {https://bookdown.org/rdpeng/rprogdatascience/}
}

@book{R-base,
  title = {R: {{A}} Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  date = {2022},
  publisher = {{R Foundation for Statistical Computing}},
  location = {{Vienna, Austria}},
  url = {https://www.R-project.org/}
}

@book{R-bayesplot,
  title = {{{bayesplot}}: {{Plotting}} for {{Bayesian}} Models},
  author = {Gabry, Jonah and Mahr, Tristan},
  date = {2022},
  url = {https://CRAN.R-project.org/package=bayesplot}
}

@book{R-blavaan,
  title = {{{blavaan}}: {{Bayesian}} Latent Variable Analysis},
  author = {Merkle, Edgar C. and Rosseel, Yves and Goodrich, Ben},
  date = {2022},
  url = {https://CRAN.R-project.org/package=blavaan}
}

@book{R-bookdown,
  title = {{{bookdown}}: {{Authoring}} Books and Technical Documents with {{R Markdown}}},
  author = {Xie, Yihui},
  date = {2022},
  url = {https://CRAN.R-project.org/package=bookdown}
}

@book{R-brms,
  title = {{{brms}}: {{Bayesian}} Regression Models Using '{{Stan}}'},
  author = {B\"urkner, Paul-Christian},
  date = {2022},
  url = {https://CRAN.R-project.org/package=brms}
}

@manual{R-broom,
  type = {manual},
  title = {{{broom}}: {{Convert}} Statistical Objects into Tidy Tibbles},
  author = {Robinson, David and Hayes, Alex and Couch, Simon},
  date = {2022},
  url = {https://CRAN.R-project.org/package=broom}
}

@book{R-dplyr,
  title = {{{dplyr}}: {{A}} Grammar of Data Manipulation},
  author = {Wickham, Hadley and Fran\c{c}ois, Romain and Henry, Lionel and M\"uller, Kirill},
  date = {2020},
  url = {https://CRAN.R-project.org/package=dplyr}
}

@manual{R-dutchmasters,
  type = {manual},
  title = {{{dutchmasters}}},
  author = {Thoen, Edwin},
  date = {2022},
  url = {https://github.com/EdwinTh/dutchmasters}
}

@book{R-GGally,
  title = {{{GGally}}: {{Extension}} to {{'ggplot2'}}},
  author = {Schloerke, Barret and Crowley, Jason and {Di Cook} and Briatte, Francois and Marbach, Moritz and Thoen, Edwin and Elberg, Amos and Larmarange, Joseph},
  date = {2021},
  url = {https://CRAN.R-project.org/package=GGally}
}

@manual{R-ggbeeswarm,
  type = {manual},
  title = {{{ggbeeswarm}}: {{Categorical}} Scatter (Violin Point) Plots},
  author = {Clarke, Erik and Sherrill-Mix, Scott},
  date = {2017},
  url = {https://CRAN.R-project.org/package=ggbeeswarm}
}

@manual{R-ggdark,
  type = {manual},
  title = {{{ggdark}}: {{Dark}} Mode for 'ggplot2' Themes},
  author = {Grantham, Neal},
  date = {2019},
  url = {https://CRAN.R-project.org/package=ggdark}
}

@book{R-ggExtra,
  title = {{{ ggExtra}}: {{Add}} Marginal Histograms to '{{ggplot2}}', and More '{{ggplot2}}' Enhancements},
  author = {Attali, Dean and Baker, Christopher},
  date = {2022},
  url = {https://CRAN.R-project.org/package=ggExtra}
}

@manual{R-ggmcmc,
  type = {manual},
  title = {{{ggmcmc}}: {{Tools}} for Analyzing {{MCMC}} Simulations from {{Bayesian}} Inference},
  author = {Fern\'andez i Mar\'in, Xavier},
  date = {2021},
  url = {https://CRAN.R-project.org/package=ggmcmc}
}

@book{R-ggplot2,
  title = {{{ggplot2}}: {{Create}} Elegant Data Visualisations Using the Grammar of Graphics},
  author = {Wickham, Hadley and Chang, Winston and Henry, Lionel and Pedersen, Thomas Lin and Takahashi, Kohske and Wilke, Claus and Woo, Kara and Yutani, Hiroaki and Dunnington, Dewey},
  date = {2022},
  url = {https://CRAN.R-project.org/package=ggplot2}
}

@book{R-ggrepel,
  title = {{{ggrepel}}: {{Automatically}} Position Non-Overlapping Text Labels with {{'ggplot2'}}},
  author = {Slowikowski, Kamil},
  date = {2022},
  url = {https://CRAN.R-project.org/package=ggrepel}
}

@book{R-ggthemes,
  title = {{{ggthemes}}: {{Extra}} Themes, Scales and Geoms for {{'ggplot2'}}},
  author = {Arnold, Jeffrey B.},
  date = {2021},
  url = {https://CRAN.R-project.org/package=ggthemes}
}

@manual{R-ghibli,
  type = {manual},
  title = {{{ghibli}}: {{Studio}} Ghibli Colour Palettes},
  author = {Henderson, Ewen},
  date = {2022},
  url = {https://CRAN.R-project.org/package=ghibli}
}

@manual{R-hrbrthemes,
  type = {manual},
  title = {{{hrbrthemes}}: {{Additional}} Themes, Theme Components and Utilities for 'Ggplot2'},
  author = {Rudis, Bob},
  date = {2020},
  url = {https://CRAN.R-project.org/package=hrbrthemes}
}

@manual{R-invgamma,
  type = {manual},
  title = {{{invgamma}}: {{The}} Inverse Gamma Distribution},
  author = {Kahle, David and Stamey, James},
  date = {2017},
  url = {https://CRAN.R-project.org/package=invgamma}
}

@book{R-lme4,
  title = {{{lme4}}: {{Linear}} Mixed-Effects Models Using {{Eigen}}' and {{S4}}},
  author = {Bates, Douglas and Maechler, Martin and Bolker, Ben and {Steven Walker}},
  date = {2022},
  url = {https://CRAN.R-project.org/package=lme4}
}

@book{R-loo,
  title = {{{loo}}: {{Efficient}} Leave-One-out Cross-Validation and {{WAIC}} for Bayesian Models},
  author = {Vehtari, Aki and Gabry, Jonah and Magnusson, Mans and Yao, Yuling and Gelman, Andrew},
  date = {2022},
  url = {https://CRAN.R-project.org/package=loo/}
}

@book{R-MASS,
  title = {{{MASS}}: {{Support}} Functions and Datasets for Venables and {{Ripley}}'s {{MASS}}},
  author = {Ripley, Brian},
  date = {2022},
  url = {https://CRAN.R-project.org/package=MASS}
}

@manual{R-metafor,
  type = {manual},
  title = {{{metafor}}: {{Meta-analysis}} Package for {{R}}},
  author = {Viechtbauer, Wolfgang},
  date = {2022},
  url = {https://CRAN.R-project.org/package=metafor}
}

@book{R-patchwork,
  title = {{{patchwork}}: {{The}} Composer of Plots},
  author = {Pedersen, Thomas Lin},
  date = {2022},
  url = {https://CRAN.R-project.org/package=patchwork}
}

@book{R-posterior,
  title = {{{posterior}}: {{Tools}} for Working with Posterior Distributions},
  author = {B\"urkner, Paul-Christian and Gabry, Jonah and Kay, Matthew and Vehtari, Aki},
  date = {2022},
  url = {https://CRAN.R-project.org/package=posterior}
}

@manual{R-priorsense,
  type = {manual},
  title = {{{priorsense}}: {{Prior}} Diagnostics and Sensitivity Analysis},
  author = {Kallioinen, Noa and B\"urkner, Paul-Christian and Paananen, Topi and Vehtari, Aki},
  date = {2022}
}

@book{R-purrr,
  title = {{{purrr}}: {{Functional}} Programming Tools},
  author = {Henry, Lionel and Wickham, Hadley},
  date = {2020},
  url = {https://CRAN.R-project.org/package=purrr}
}

@book{R-rcartocolor,
  title = {{{rcartocolor}}: '{{CARTOColors}}' Palettes},
  author = {Nowosad, Jakub},
  date = {2019},
  url = {https://CRAN.R-project.org/package=rcartocolor}
}

@book{R-rethinking,
  title = {{{rethinking}} {{R}} Package},
  author = {McElreath, Richard},
  date = {2020},
  url = {https://xcelab.net/rm/software/}
}

@manual{R-rstanarm,
  type = {manual},
  title = {{{rstanarm}}: {{Bayesian}} Applied Regression Modeling via Stan},
  author = {Gabry, Jonah and Goodrich, Ben},
  date = {2022},
  url = {https://CRAN.R-project.org/package=rstanarm}
}

@book{R-tibble,
  title = {{{tibble}}: {{Simple}} Data Frames},
  author = {M\"uller, Kirill and Wickham, Hadley},
  date = {2022},
  url = {https://CRAN.R-project.org/package=tibble}
}

@book{R-tidybayes,
  title = {{{tidybayes}}: {{Tidy}} Data and 'geoms' for {{Bayesian}} Models},
  author = {Kay, Matthew},
  date = {2022},
  url = {https://CRAN.R-project.org/package=tidybayes}
}

@book{R-tidyr,
  title = {{{tidyr}}: {{Tidy}} Messy Data},
  author = {Wickham, Hadley and Henry, Lionel},
  date = {2020},
  url = {https://CRAN.R-project.org/package=tidyr}
}

@book{R-tidyverse,
  title = {{{tidyverse}}: {{Easily}} Install and Load the 'Tidyverse'},
  author = {Wickham, Hadley},
  date = {2022},
  url = {https://CRAN.R-project.org/package=tidyverse}
}

@book{R-tigris,
  title = {Tigris: {{Load Census TIGER}}/{{Line}} Shapefiles},
  author = {Walker, Kyle},
  date = {2022},
  url = {https://github.com/walkerke/tigris}
}

@book{R-urbnmapr,
  title = {{{urbnmapr}}: {{State}} and County Maps with {{Alaska}} and {{Hawaii}}},
  author = {{Urban Institute}},
  date = {2020},
  url = {https://github.com/UrbanInstitute/urbnmapr}
}

@manual{R-viridis,
  type = {manual},
  title = {{{viridis}}: {{Default}} Color Maps from 'Matplotlib'},
  author = {Garnier, Simon},
  date = {2021},
  url = {https://CRAN.R-project.org/package=viridis}
}

@manual{R-wesanderson,
  type = {manual},
  title = {{{wesanderson}}: {{A Wes Anderson}} Palette Generator},
  author = {Ram, Karthik and Wickham, Hadley},
  date = {2018},
  url = {https://CRAN.R-project.org/package=wesanderson}
}

@article{robertShortHistoryMarkov2011,
  title = {A Short History of {{Markov}} Chain {{Monte Carlo}}: {{Subjective}} Recollections from Incomplete Data},
  shorttitle = {A {{Short History}} of {{Markov Chain Monte Carlo}}},
  author = {Robert, Christian and Casella, George},
  date = {2011},
  journaltitle = {Statistical Science},
  volume = {26},
  number = {1},
  pages = {102--115},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0883-4237},
  url = {https://arxiv.org/pdf/0808.2902.pdf},
  urldate = {2020-08-19},
  abstract = {We attempt to trace the history and development of Markov chain Monte Carlo (MCMC) from its early inception in the late 1940s through its use today. We see how the earlier stages of Monte Carlo (MC, not MCMC) research have led to the algorithms currently in use. More importantly, we see how the development of this methodology has not only changed our solutions to problems, but has changed the way we think about problems.}
}

@article{rstanarm2018,
  title = {Joint Longitudinal and Time-to-Event Models via {{Stan}}.},
  author = {Brilleman, SL and Crowther, MJ and Moreno-Betancur, M and Buros Novik, J and Wolfe, R},
  date = {2018},
  url = {https://github.com/stan-dev/stancon_talks/}
}

@article{rudisViridisColorPalettes2018,
  title = {The Viridis Color Palettes},
  author = {Rudis, Bob and Ross, Noam and Garnier, Simon},
  date = {2018-03},
  url = {https://cran.r-project.org/package=viridis/vignettes/intro-to-viridis.html},
  urldate = {2018-03-29},
  langid = {english}
}

@article{silkChimpanzeesAreIndifferent2005,
  title = {Chimpanzees Are Indifferent to the Welfare of Unrelated Group Members},
  author = {Silk, Joan B. and Brosnan, Sarah F. and Vonk, Jennifer and Henrich, Joseph and Povinelli, Daniel J. and Richardson, Amanda S. and Lambeth, Susan P. and Mascaro, Jenny and Schapiro, Steven J.},
  date = {2005-10},
  journaltitle = {Nature},
  volume = {437},
  number = {7063},
  pages = {1357--1359},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/nature04243},
  url = {https://www.nature.com/articles/nature04243},
  urldate = {2020-06-16},
  abstract = {There is plenty of evidence \textemdash{} some of it cited in this week's Review Article \textemdash{} that humans care about the welfare of others and will provide costly assistance even to strangers. Frans de Waal has argued that 'other-regarding sentiments' may be deep-rooted in primate evolutionary history. But a test for such behaviour in chimpanzees has drawn a blank. They spurn the chance to deliver benefits to unrelated but familiar individuals at no cost to themselves, cooperating only with their kin and partners.},
  issue = {7063},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/36JBUZ32/nature04243.html}
}

@article{simmonsFalsepositivePsychologyUndisclosed2011,
  title = {False-Positive Psychology: {{Undisclosed}} Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant},
  shorttitle = {False-{{Positive Psychology}}},
  author = {Simmons, Joseph P. and Nelson, Leif D. and Simonsohn, Uri},
  date = {2011-11-01},
  journaltitle = {Psychological Science},
  shortjournal = {Psychol Sci},
  volume = {22},
  number = {11},
  pages = {1359--1366},
  publisher = {{SAGE Publications Inc}},
  issn = {0956-7976},
  doi = {10.1177/0956797611417632},
  url = {https://journals.sagepub.com/doi/full/10.1177/0956797611417632},
  urldate = {2020-05-23},
  abstract = {In this article, we accomplish two things. First, we show that despite empirical psychologists' nominal endorsement of a low rate of false-positive findings ({$\leq$} .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/N85FNDMV/Simmons et al. - 2011 - False-Positive Psychology Undisclosed Flexibility.pdf}
}

@online{standevelopmentteamRStanInterfaceStan2020,
  title = {{{RStan}}: The {{R}} Interface to {{Stan}}},
  author = {{Stan Development Team}},
  date = {2020-02-10},
  url = {https://cran.r-project.org/web/packages/rstan/vignettes/rstan.html},
  urldate = {2020-05-22},
  file = {/Users/solomonkurz/Zotero/storage/UNLVDTJP/rstan.html}
}

@misc{standevelopmentteamRStanInterfaceStan2022,
  title = {{{RStan}}: {{The R}} Interface to {{Stan}}},
  author = {{Stan Development Team}},
  date = {2022-04},
  url = {https://CRAN.R-project.org/package=rstan/vignettes/rstan.html},
  urldate = {2022-05-14}
}

@misc{standevelopmentteamRStanInterfaceStan2023,
  title = {{{RStan}}: {{The R Interface}} to {{Stan}}},
  author = {{Stan Development Team}},
  date = {2023-01-16},
  url = {https://CRAN.R-project.org/package=rstan/vignettes/rstan.html},
  urldate = {2023-01-19}
}

@book{standevelopmentteamStanFunctionsReference2021,
  title = {Stan Functions Reference},
  author = {{Stan Development Team}},
  date = {2021},
  url = {https://mc-stan.org/docs/2_26/functions-reference/index.html},
  urldate = {2020-06-21},
  abstract = {Reference for the functions defined in the Stan math library and available in the Stan programming language.},
  file = {/Users/solomonkurz/Zotero/storage/IGIXTGX4/index.html}
}

@book{standevelopmentteamStanFunctionsReference2022,
  title = {Stan Functions Reference, {{Version}} 2.31},
  author = {{Stan Development Team}},
  date = {2022},
  url = {https://mc-stan.org/docs/functions-reference/},
  urldate = {2022-09-26}
}

@article{subramanianAverageTreatmentEffect2018a,
  title = {The ``Average'' Treatment Effect: {{A}} Construct Ripe for Retirement. {{A}} Commentary on {{Deaton}} and {{Cartwright}}},
  shorttitle = {The ``Average'' Treatment Effect},
  author = {Subramanian, S. V. and Kim, Rockli and Christakis, Nicholas A.},
  date = {2018-08-01},
  journaltitle = {Social Science \& Medicine},
  shortjournal = {Social Science \& Medicine},
  series = {Randomized {{Controlled Trials}} and {{Evidence-based Policy}}: {{A Multidisciplinary Dialogue}}},
  volume = {210},
  pages = {77--82},
  issn = {0277-9536},
  doi = {10.1016/j.socscimed.2018.04.027},
  url = {http://www.sciencedirect.com/science/article/pii/S0277953618301941},
  urldate = {2020-06-16},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/KB9NJ647/S0277953618301941.html}
}

@book{tufteVisualDisplayQuantitative2001,
  title = {The Visual Display of Quantitative Information},
  author = {Tufte, Edward R.},
  date = {2001-05-01},
  edition = {Second Edition},
  publisher = {{Graphics Press}},
  location = {{Cheshire, Conn}},
  url = {https://www.edwardtufte.com/tufte/books_vdqi},
  abstract = {The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.},
  isbn = {978-1-930824-13-3},
  langid = {english},
  pagetotal = {200}
}

@article{unicef2014hidden,
  title = {Hidden in Plain Sight: {{A}} Statistical Analysis of Violence against Children},
  author = {{UNICEF}},
  date = {2014},
  publisher = {{UNICEF}},
  url = {https://www.unicef.org/publications/index_74865.html}
}

@book{vanbuurenFlexibleImputationMissing2018,
  title = {Flexible Imputation of Missing Data},
  shorttitle = {Https},
  author = {van Buuren, Stef},
  options = {useprefix=true},
  date = {2018},
  edition = {Second Edition},
  publisher = {{CRC Press}},
  url = {https://stefvanbuuren.name/fimd/},
  urldate = {2020-09-19},
  isbn = {978-0-429-96034-5 0-429-96034-4},
  file = {/Users/solomonkurz/Zotero/storage/VIE7D8L2/fimd.html}
}

@article{vanderleeGender2015,
  title = {Gender Contributes to Personal Research Funding Success in {{The Netherlands}}},
  author = {Van der Lee, Romy and Ellemers, Naomi},
  date = {2015},
  journaltitle = {Proceedings of the National Academy of Sciences},
  volume = {112},
  number = {40},
  pages = {12349--12353},
  publisher = {{National Acad Sciences}},
  doi = {10.1073/pnas.1510159112},
  url = {https://doi.org/10.1073/pnas.1510159112}
}

@online{vehtariBayesianStackingPseudoBMA,
  title = {Bayesian Stacking and Pseudo-{{BMA}} Weights Using the Loo Package},
  author = {Vehtari, Aki and Gabry, Jonah},
  date = {2022-03-23},
  url = {https://CRAN.R-project.org/package=loo/vignettes/loo2-weights.html}
}

@article{vehtariPracticalBayesianModel2017,
  title = {Practical {{Bayesian}} Model Evaluation Using Leave-One-out Cross-Validation and {{WAIC}}},
  author = {Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
  date = {2017-09-01},
  journaltitle = {Statistics and Computing},
  shortjournal = {Stat Comput},
  volume = {27},
  number = {5},
  pages = {1413--1432},
  issn = {1573-1375},
  doi = {10.1007/s11222-016-9696-4},
  url = {https://arxiv.org/pdf/1507.04544.pdf},
  urldate = {2020-06-03},
  abstract = {Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values. LOO and WAIC have various advantages over simpler estimates of predictive error such as AIC and DIC but are less used in practice because they involve additional computational steps. Here we lay out fast and stable computations for LOO and WAIC that can be performed using existing simulation draws. We introduce an efficient computation of LOO using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. Although WAIC is asymptotically equal to LOO, we demonstrate that PSIS-LOO is more robust in the finite case with weak priors or influential observations. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors and for comparison of predictive errors between two models. We implement the computations in an R package called loo and demonstrate using models fit with the Bayesian inference package Stan.},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/I7HY567V/Vehtari et al. - 2017 - Practical Bayesian model evaluation using leave-on.pdf}
}

@unpublished{vehtariRanknormalizationFoldingLocalization2019,
  title = {Rank-Normalization, Folding, and Localization: {{An}} Improved {$\widehat{R}$} for Assessing Convergence of {{MCMC}}},
  author = {Vehtari, Aki and Gelman, Andrew and Simpson, Daniel and Carpenter, Bob and B\"urkner, Paul-Christian},
  date = {2019},
  eprint = {1903.08008},
  eprinttype = {arxiv},
  url = {https://arxiv.org/abs/1903.08008?},
  archiveprefix = {arXiv}
}

@article{vehtariRanknormalizationFoldingLocalization2021,
  title = {Rank-Normalization, Folding, and Localization: {{An}} Improved {$\widehat{R}$} for Assessing Convergence of {{MCMC}} (with {{Discussion}})},
  author = {Vehtari, Aki and Gelman, Andrew and Simpson, Daniel and Carpenter, Bob and B\"urkner, Paul-Christian},
  date = {2021},
  journaltitle = {Bayesian analysis},
  volume = {16},
  number = {2},
  pages = {667--718},
  publisher = {{International Society for Bayesian Analysis}},
  doi = {10.1214/20-BA1221}
}

@online{vehtariUsingLooPackage2020,
  title = {Using the Loo Package (Version {$>$}= 2.0.0)},
  author = {Vehtari, Aki and Gabry, Jonah},
  date = {2020-07-14},
  url = {https://CRAN.R-project.org/package=loo/vignettes/loo2-example.html},
  urldate = {2020-09-15}
}

@misc{vehtariUsingLooPackage2022,
  title = {Using the Loo Package ({{Version}} {$>$}= 2.0.0)},
  author = {Vehtari, Aki and Gabry, Jonah},
  date = {2022-03},
  url = {https://CRAN.R-project.org/package=loo/vignettes/loo2-example.html},
  urldate = {2022-04-15}
}

@artwork{vermeerGirlPearlEarring1665,
  title = {Girl with a Pearl Earring},
  author = {Vermeer, Johannes},
  date = {1665}
}

@article{viechtbauerMetafor2010,
  title = {Conducting Meta-Analyses in {{R}} with the {{metafor}} Package},
  author = {Viechtbauer, Wolfgang},
  date = {2010},
  journaltitle = {Journal of Statistical Software},
  volume = {36},
  number = {3},
  pages = {1--48},
  url = {https://www.jstatsoft.org/v36/i03/}
}

@article{volkerNoEvidenceThat2015,
  title = {No Evidence That Gender Contributes to Personal Research Funding Success in {{The Netherlands}}: {{A}} Reaction to van Der {{Lee}} and {{Ellemers}}},
  shorttitle = {No Evidence That Gender Contributes to Personal Research Funding Success in {{The Netherlands}}},
  author = {Volker, Beate and Steenbeek, Wouter},
  date = {2015-12-22},
  journaltitle = {Proceedings of the National Academy of Sciences},
  shortjournal = {PNAS},
  volume = {112},
  number = {51},
  eprint = {26647179},
  eprinttype = {pmid},
  pages = {E7036-E7037},
  publisher = {{National Academy of Sciences}},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1519046112},
  url = {https://www.pnas.org/content/112/51/E7036},
  urldate = {2020-10-09},
  abstract = {A recent PNAS article (1) argues that success rates for attaining research grants are gender-biased. However, the overall gender effect borders on statistical significance, despite the large sample. Moreover, their conclusion could be a prime example of Simpson's paradox (2, 3); if a higher percentage of women apply for grants in more competitive scientific disciplines (i.e., with low application success rates for both men and women), then an analysis across all disciplines could incorrectly show ``evidence'' of gender inequality. Indeed, the social sciences \ldots{}  [{$\carriagereturn$}][1]1To whom correspondence should be addressed. Email: b.volker\{at\}uva.nl.  [1]: \#xref-corresp-1-1},
  langid = {english},
  file = {/Users/solomonkurz/Zotero/storage/R4NPBJF6/Volker and Steenbeek - 2015 - No evidence that gender contributes to personal re.pdf;/Users/solomonkurz/Zotero/storage/MSXUKZ7M/E7036.html}
}

@article{voneshCompensatoryLarvalResponses2005,
  title = {Compensatory Larval Responses Shift Trade-Offs Associated with Predator-Induced Hatching Plasticity},
  author = {Vonesh, James R. and Bolker, Benjamin M.},
  date = {2005},
  journaltitle = {Ecology},
  volume = {86},
  number = {6},
  pages = {1580--1591},
  issn = {1939-9170},
  doi = {10.1890/04-0535},
  url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/04-0535},
  urldate = {2020-07-01},
  abstract = {Many species with complex life histories can respond to risk by adaptively altering the timing of key life history switch points, including hatching. It is generally thought that such hatching plasticity involves a trade-off between embryonic and hatchling predation risk, e.g., hatching early to escape egg predation comes at the cost of increased vulnerability to hatchling predators. However, most empirical work has focused on simply detecting predator-induced hatching responses or on the short-term consequences of hatching plasticity. Short-term studies may not allow sufficient time for hatchlings to exhibit compensatory responses, which may extend to subsequent life stages and could alter the nature of the trade-offs associated with hatching plasticity. In this study, we examine the consequences of predator-induced hatching plasticity through the larval stage to metamorphosis in the East African reed frog, Hyperolius spinigularis. To do this we conducted an experiment in which we manipulated initial larval size and density (mimicking the effects of egg predators) and the presence of aquatic predators. We expected that predator-induced hatchlings (because they are less developed and smaller) would experience higher per capita predation rates and a longer larval period and thus would exhibit lower survival to metamorphosis in the presence of aquatic predators than larger, more developed, later hatched larvae. Surprisingly, we found that predator-induced hatchlings survived better, not worse, than hatchlings from undisturbed clutches. These results motivated us to develop a model parameterized from additional experiments to explore whether a combination of mechanisms, compensatory growth, and density- and size-specific predation, could give rise to this pattern. Predicted survival probabilities from the model with compensatory growth were consistent with those from the field experiment: early hatched larvae grew more rapidly through vulnerable size classes than later hatched larvae, resulting in higher survival at metamorphosis. Thus, in this system, there does not appear to be a trade-off in vulnerability between egg and larval predators. Instead, our results suggest that the cost that balances the survival benefit of hatching early to evade egg predators arises later in the life history, as a result of smaller size at metamorphosis.},
  langid = {english},
  keywords = {compensatory growth,density-mediated indirect interaction (DMII),functional response,hatching plasticity,Hyperolius spinigularis,multiple predators,phenotypic plasticity,size-selective predation,trait-mediated indirect interaction (TMII)},
  annotation = {\_eprint: https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/04-0535},
  file = {/Users/solomonkurz/Zotero/storage/HTIQ7YL3/04-0535.html}
}

@article{wassersteinASAStatementonPValues2016,
  title = {The {{ASA}} Statement on P-Values: {{Context}}, Process, and Purpose},
  author = {Wasserstein, Ronald L and Lazar, Nicole A},
  date = {2016},
  volume = {70},
  number = {2},
  pages = {129--133},
  publisher = {{Taylor \& Francis}},
  doi = {10.1080/00031305.2016.1154108}
}

@article{wassersteinASAStatementPvalues2016,
  title = {The {{ASA}} Statement on P-Values: {{Context}}, Process, and Purpose},
  shorttitle = {The {{ASA Statement}} on P-{{Values}}},
  author = {Wasserstein, Ronald L. and Lazar, Nicole A.},
  date = {2016-04-02},
  journaltitle = {The American Statistician},
  shortjournal = {null},
  volume = {70},
  number = {2},
  pages = {129--133},
  publisher = {{Taylor \& Francis}},
  issn = {0003-1305},
  doi = {10.1080/00031305.2016.1154108},
  url = {https://amstat.tandfonline.com/doi/full/10.1080/00031305.2016.1154108},
  urldate = {2020-10-05},
  file = {/Users/solomonkurz/Zotero/storage/VX4G5RS8/Wasserstein and Lazar - 2016 - The ASA Statement on p-Values Context, Process, a.pdf;/Users/solomonkurz/Zotero/storage/XGDUTFLG/00031305.2016.html}
}

@article{watanabeAsymptoticEquivalenceBayes2010,
  title = {Asymptotic Equivalence of {{Bayes}} Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory},
  author = {Watanabe, Sumio},
  date = {2010},
  journaltitle = {Journal of Machine Learning Research},
  volume = {11},
  number = {116},
  pages = {3571--3594},
  issn = {1533-7928},
  url = {http://jmlr.org/papers/v11/watanabe10a.html},
  urldate = {2020-06-12},
  file = {/Users/solomonkurz/Zotero/storage/4HA4VYLM/Watanabe - 2010 - Asymptotic Equivalence of Bayes Cross Validation a.pdf;/Users/solomonkurz/Zotero/storage/MSKBHX5F/watanabe10a.html}
}

@book{wickhamGgplot2ElegantGraphics2016,
  title = {{{ggplot2}}: {{Elegant}} Graphics for Data Analysis},
  author = {Wickham, Hadley},
  date = {2016},
  publisher = {{Springer-Verlag New York}},
  url = {https://ggplot2-book.org/},
  isbn = {978-3-319-24277-4}
}

@book{wickhamTidyverseStyleGuide2020,
  title = {The Tidyverse Style Guide},
  author = {Wickham, Hadley},
  date = {2020},
  url = {https://style.tidyverse.org/}
}

@article{wickhamWelcomeTidyverse2019,
  title = {Welcome to the Tidyverse},
  author = {Wickham, Hadley and Averick, Mara and Bryan, Jennifer and Chang, Winston and McGowan, Lucy D'Agostino and Fran\c{c}ois, Romain and Grolemund, Garrett and Hayes, Alex and Henry, Lionel and Hester, Jim and Kuhn, Max and Pedersen, Thomas Lin and Miller, Evan and Bache, Stephan Milton and M\"uller, Kirill and Ooms, Jeroen and Robinson, David and Seidel, Dana Paige and Spinu, Vitalie and Takahashi, Kohske and Vaughan, Davis and Wilke, Claus and Woo, Kara and Yutani, Hiroaki},
  date = {2019},
  journaltitle = {Journal of Open Source Software},
  volume = {4},
  number = {43},
  pages = {1686},
  doi = {10.21105/joss.01686}
}

@book{wilkeFundamentalsDataVisualization2019,
  title = {Fundamentals of Data Visualization},
  author = {Wilke, Claus O.},
  date = {2019},
  url = {https://clauswilke.com/dataviz/},
  urldate = {2020-08-24},
  abstract = {A guide to making visualizations that accurately reflect the data, tell a story, and look professional.},
  file = {/Users/solomonkurz/Zotero/storage/URYRTXWA/dataviz.html}
}

@article{williamsBayesianMetaanalysisWeakly2018,
  title = {Bayesian Meta-Analysis with Weakly Informative Prior Distributions},
  author = {Williams, Donald R. and Rast, Philippe and B\"urkner, Paul-Christian},
  date = {2018-01-10},
  publisher = {{PsyArXiv}},
  doi = {10.31234/osf.io/7tbrm},
  url = {https://psyarxiv.com/7tbrm/},
  urldate = {2020-10-11},
  abstract = {Developing meta-analytic methods is an important goal for psychological science. When there are few studies in particular, commonly used methods have several limitations, most notably of which is underestimating between-study variability. Although Bayesian methods are often recommended for small sample situations, their performance has not been thoroughly examined in the context of meta-analysis. Here, we characterize and apply weakly informative priors for estimating meta-analytic models and demonstrate with extensive simulations that fully Bayesian methods overcome boundary estimates of exactly zero between-study variance, better maintain error rates, and have lower frequentist risk according to Kullback-Leibler divergence. While our results show that combining evidence with few studies is non-trivial, we argue that this is an important goal that deserves further consideration in psychology. Further, we suggest that frequentist properties can provide important information for Bayesian modeling. We conclude with meta-analytic guidelines for applied researchers that can be implemented with the provided computer code.}
}

@article{williamsBayesianMultivariateMixedeffects2019a,
  title = {Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations among Affective Traits, States, and Physical Activity},
  author = {Williams, Donald R. and Liu, Siwei and Martin, Stephen R. and Rast, Philippe},
  date = {2019-03-27T17:06:37},
  publisher = {{PsyArXiv}},
  doi = {10.31234/osf.io/4kfjp},
  url = {https://psyarxiv.com/4kfjp/},
  urldate = {2020-08-05},
  abstract = {Intensive longitudinal studies and experience sampling methods are becoming more common in psychology. While they provide a unique opportunity to ask novel questions about within-person processes relating to personality, there is a lack of methods specifically built to characterize the interplay between traits and states. We thus introduce a Bayesian multivariate mixed-effects location scale model (M-MELSM). The formulation can simultaneously model both personality traits (the location) and states (the scale) for multivariate data common to personality research. Variables can be included to predict either (or both) the traits and states, in addition to estimating random effects therein. This provides correlations between location and scale random effects, both across and within each outcome, which allows for characterizing relations between any number personality traits and the corresponding states. We take a \textbackslash textit\{fully\} Bayesian approach, not only to make estimation possible, but also because  it provides the necessary information for use in psychological applications such as hypothesis testing. To illustrate the model we use data from 194 individuals that provided daily ratings of negative and positive affect, as well as their psychical activity in the form of step counts over 100 consecutive days. We describe the fitted model, where we emphasize, with visualization, the richness of information provided by the M-MELSM. We demonstrate Bayesian hypothesis testing for the correlations between the random effects. We conclude by discussing limitations of the MELSM in general and extensions to the M-MELSM specifically for personality research.},
  file = {/Users/solomonkurz/Zotero/storage/6ZTN3QZI/Williams et al. - 2019 - Bayesian Multivariate Mixed-Effects Location Scale.pdf;/Users/solomonkurz/Zotero/storage/AZQVGTM4/4kfjp.html}
}

@article{williamsBayesianMultivariateMixedeffects2021,
  title = {Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations among Affective Traits, States, and Physical Activity},
  author = {Williams, Donald R and Martin, Stephen R and Liu, Siwei and Rast, Philippe},
  date = {2021},
  journaltitle = {European Journal of Psychological Assessment},
  publisher = {{Hogrefe Publishing}},
  doi = {10.1027/1015-5759/a000624}
}

@article{winermanTrendsReport2017,
  title = {Trends Report: {{Psychologists}} Embrace Open Science},
  author = {Winerman, Lea},
  date = {2017},
  journaltitle = {Monitor on Psychology},
  volume = {48},
  number = {10},
  publisher = {{American Psychological Association}},
  url = {https://www.apa.org/monitor/2017/11/trends-open-science}
}

@book{xieMarkdownDefinitiveGuide2020,
  title = {R Markdown: {{The}} Definitive Guide},
  author = {Xie, Yihui and Allaire, J. J. and Grolemund, Garrett},
  date = {2020},
  publisher = {{Chapman and Hall/CRC}},
  url = {https://bookdown.org/yihui/rmarkdown/}
}

@article{yaoUsingStackingAverage2018,
  title = {Using Stacking to Average {{Bayesian}} Predictive Distributions (with Discussion)},
  author = {Yao, Yuling and Vehtari, Aki and Simpson, Daniel and Gelman, Andrew},
  date = {2018},
  journaltitle = {Bayesian Analysis},
  volume = {13},
  number = {3},
  pages = {917--1007},
  publisher = {{International Society for Bayesian Analysis}},
  doi = {10.1214/17-BA1091},
  url = {https://projecteuclid.org/download/pdfview_1/euclid.ba/1516093227}
}

@book{zotero-1870,
  type = {book}
}

@book{ZoteroYourPersonal2020,
  title = {Zotero},
  author = {{Roy Rosenzweig Center for History and New Media}},
  date = {2020},
  url = {https://www.zotero.org/},
  urldate = {2020-05-19},
  file = {/Users/solomonkurz/Zotero/storage/SUAPYKGF/www.zotero.org.html}
}