book.bib

@book{DeMoivre1756,
  author = {Abraham de Moivre},
  title = {The doctrine of chances: or, A method of calculating the probabilities of events in play},
  year = {1756},
  URL = {https://archive.org/details/doctrineofchance00moiv/mode/2up}
}

@book{deFinetti1958,
  author = {de Finetti, B.},
  year = {1958}, 
  title = {Foundations of probability}, 
  booktitle = {Philosophy in the Mid-century}, 
  pages = {140–-147},
  publisher = {La Nuova Italia Editrice},
  address = {Florence}
}  


@article{Rest1984,
author = {James R. Rest},
title ={Research on Moral Development: Implications for Training Counseling Psychologists},
journal = {The Counseling Psychologist},
volume = {12},
number = {3},
pages = {19-29},
year = {1984},
doi = {10.1177/0011000084123003},

URL = { 
        https://doi.org/10.1177/0011000084123003
    
},
eprint = { 
        https://doi.org/10.1177/0011000084123003
    
}

}

@article{Peirce1884,
	author = {C. S. Peirce and Joseph Jastrow},
	pages = {75--83},
	year = {1884},
	title = {On Small Differences in Sensation},
	volume = {3},
	journal = {Memoirs of the National Academy of Sciences}
}


@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}},
  year = {1941},
  pages = {19--29},
  publisher = {{New Directions}},
  address = {{New York}}
}

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

@book{agrestiFoundationsLinearGeneralized2015,
  title = {Foundations of Linear and Generalized Linear Models},
  author = {Agresti, Alan},
  year = {2015},
  month = jan,
  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.},
  googlebooks = {dgIzBgAAQBAJ},
  isbn = {978-1-118-73005-8},
  keywords = {Mathematics / Probability \& Statistics / General,Mathematics / Probability \& Statistics / Stochastic Processes},
  language = {en}
}

@incollection{akaike1998information,
  title = {Information Theory and an Extension of the Maximum Likelihood Principle},
  booktitle = {Selected Papers of {{Hirotugu Akaike}}},
  author = {Akaike, Hirotogu},
  year = {1998},
  pages = {199--213},
  publisher = {{Springer}},
  url = {https://www.springer.com/gp/book/9780387983554}
}

@article{amrheinScientistsRiseStatistical2019,
  title = {Scientists Rise up against Statistical Significance},
  author = {Amrhein, Valentin and Greenland, Sander and McShane, Blake},
  year = {2019},
  month = mar,
  volume = {567},
  pages = {305--307},
  publisher = {{Nature Publishing Group}},
  doi = {10.1038/d41586-019-00857-9},
  url = {https://www.nature.com/articles/d41586-019-00857-9},
  urldate = {2020-05-21},
  abstract = {Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.},
  copyright = {2020 Nature},
  file = {/Users/solomonkurz/Zotero/storage/5JQ3EHZV/Amrhein et al. - 2019 - Scientists rise up against statistical significanc.pdf;/Users/solomonkurz/Zotero/storage/IWDNAU3J/d41586-019-00857-9.html},
  journal = {Nature},
  language = {en},
  number = {7748}
}

@article{angristDoesCompulsorySchool1991,
  title = {Does Compulsory School Attendance Affect Schooling and Earnings?},
  author = {Angrist, Joshua D. and Keueger, Alan B.},
  year = {1991},
  month = nov,
  volume = {106},
  pages = {979--1014},
  publisher = {{Oxford Academic}},
  issn = {0033-5533},
  doi = {10.2307/2937954},
  url = {https://academic.oup.com/qje/article/106/4/979/1873496},
  urldate = {2020-08-01},
  abstract = {Abstract.  We establish that season of birth is related to educational attainment because of school start age policy and compulsory school attendance laws. Indi},
  file = {/Users/solomonkurz/Zotero/storage/WG5T6RJ7/Angrist and Keueger - 1991 - Does Compulsory School Attendance Affect Schooling.pdf;/Users/solomonkurz/Zotero/storage/WGBTDCNB/1873496.html},
  journal = {The Quarterly Journal of Economics},
  language = {en},
  number = {4}
}

@article{ape2019,
  title = {{{ape}} 5.0: An Environment for Modern Phylogenetics and Evolutionary Analyses in {{R}}},
  author = {Paradis, E. and Schliep, K.},
  year = {2019},
  volume = {35},
  pages = {526--528},
  doi = {10.1093/bioinformatics/bty633},
  url = {https://academic.oup.com/bioinformatics/article/35/3/526/5055127},
  journal = {Bioinformatics}
}

@article{atkinsTutorialCountRegression2013,
  title = {A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data},
  author = {Atkins, David C. and Baldwin, Scott A. and Zheng, Cheng and Gallop, Robert J. and Neighbors, Clayton},
  year = {2013},
  month = mar,
  volume = {27},
  pages = {166--177},
  issn = {0893-164X},
  doi = {10.1037/a0029508},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3513584/},
  urldate = {2020-06-30},
  abstract = {Critical research questions in the study of addictive behaviors concern how these behaviors change over time - either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a .},
  file = {/Users/solomonkurz/Zotero/storage/3EXIKU7I/Atkins et al. - 2013 - A tutorial on count regression and zero-altered co.pdf},
  journal = {Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors},
  number = {1},
  pmcid = {PMC3513584},
  pmid = {22905895}
}

@article{barrettAnIntroduction2020,
  title = {An Introduction to Ggdag},
  author = {Barrett, Malcolm},
  year = {2020},
  month = feb,
  url = {https://CRAN.R-project.org/package=ggdag/vignettes/intro-to-ggdag.html},
  urldate = {2020-05-31},
  language = {English}
}

@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.},
  year = {2013},
  month = apr,
  volume = {68},
  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.},
  file = {/Users/solomonkurz/Zotero/storage/FHRVR92C/Barr et al. - 2013 - Random effects structure for confirmatory hypothes.pdf;/Users/solomonkurz/Zotero/storage/7SG2QQCA/S0749596X12001180.html},
  journal = {Journal of Memory and Language},
  keywords = {Generalization,Linear mixed-effects models,Monte Carlo simulation,Statistics},
  language = {en},
  number = {3}
}

@article{betancourtBayesSparse2018,
  title = {Bayes Sparse Regression},
  author = {Betancourt, Michael},
  year = {2018},
  month = mar,
  url = {https://betanalpha.github.io/assets/case_studies/bayes_sparse_regression.html},
  language = {English}
}

@article{betancourtRobustGaussianProcesses2017,
  title = {Robust {{Gaussian}} Processes in {{Stan}}},
  author = {Betancourt, Michael},
  year = {2017},
  month = oct,
  url = {https://betanalpha.github.io/assets/case_studies/gp_part3/part3.html},
  urldate = {2020-08-18},
  file = {/Users/solomonkurz/Zotero/storage/YI47KGAQ/part3.html},
  language = {English}
}

@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.},
  year = {1975},
  month = feb,
  volume = {187},
  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.},
  chapter = {Articles},
  copyright = {1975 by the American Association for the Advancement of Science},
  file = {/Users/solomonkurz/Zotero/storage/XW4GACMB/398.html},
  journal = {Science},
  language = {en},
  number = {4175},
  pmid = {17835295}
}

@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}},
  year = {1941},
  pages = {19--29},
  publisher = {{New Directions}},
  address = {{New York}}
}

@book{brms2020RM,
  title = {{{brms}} Reference Manual, {{Version}} 2.13.5},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2020},
  url = {https://CRAN.R-project.org/package=brms/brms.pdf}
}

@book{bugs2003UM,
  title = {{{WinBUGS}} User Manual},
  author = {Spiegelhalter, David and Thomas, Andrew and Best, Nicky and Lunn, Dave},
  year = {2003},
  month = jan,
  url = {https://www.mrc-bsu.cam.ac.uk/wp-content/uploads/manual14.pdf}
}

@article{Bürkner2020Define,
  title = {Define Custom Response Distributions with Brms},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2020},
  month = feb,
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_customfamilies.html}
}

@misc{Bürkner2020Distributional,
  title = {Estimating Distributional Models with Brms},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2020},
  month = feb,
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_distreg.html}
}

@article{Bürkner2020Monotonic,
  title = {Estimating Monotonic Effects with  Brms},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2020},
  month = may,
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_monotonic.html}
}

@article{Bürkner2020Multivariate,
  title = {Estimating Multivariate Models with Brms},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2020},
  month = feb,
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_multivariate.html}
}

@article{Bürkner2020Non_linear,
  title = {Estimating Non-Linear Models with Brms},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2020},
  month = feb,
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_nonlinear.html}
}

@article{Bürkner2020Parameterization,
  title = {Parameterization of Response Distributions in Brms},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2020},
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_families.html}
}

@article{Bürkner2020Phylogenetic,
  title = {Estimating Phylogenetic Multilevel Models with Brms},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2020},
  month = jul,
  url = {https://CRAN.R-project.org/package=brms/vignettes/brms_phylogenetics.html}
}

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

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

@article{burknerModellingMonotonicEffects2020,
  title = {Modelling Monotonic Effects of Ordinal Predictors in {{Bayesian}} Regression Models},
  author = {B{\"u}rkner, Paul-Christian and Charpentier, Emmanuel},
  year = {2020},
  issn = {2044-8317},
  doi = {10.1111/bmsp.12195},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/bmsp.12195},
  urldate = {2020-06-28},
  abstract = {Ordinal predictors are commonly used in regression models. They are often incorrectly treated as either nominal or metric, thus under- or overestimating the information contained. Such practices may lead to worse inference and predictions compared to methods which are specifically designed for this purpose. We propose a new method for modelling ordinal predictors that applies in situations in which it is reasonable to assume their effects to be monotonic. The parameterization of such monotonic effects is realized in terms of a scale parameter b representing the direction and size of the effect and a simplex parameter modelling the normalized differences between categories. This ensures that predictions increase or decrease monotonically, while changes between adjacent categories may vary across categories. This formulation generalizes to interaction terms as well as multilevel structures. Monotonic effects may be applied not only to ordinal predictors, but also to other discrete variables for which a monotonic relationship is plausible. In simulation studies we show that the model is well calibrated and, if there is monotonicity present, exhibits predictive performance similar to or even better than other approaches designed to handle ordinal predictors. Using Stan, we developed a Bayesian estimation method for monotonic effects which allows us to incorporate prior information and to check the assumption of monotonicity. We have implemented this method in the R package brms, so that fitting monotonic effects in a fully Bayesian framework is now straightforward.},
  copyright = {\textcopyright{} 2020 The British Psychological Society},
  file = {/Users/solomonkurz/Zotero/storage/32MU9XU6/bmsp.html},
  journal = {British Journal of Mathematical and Statistical Psychology},
  keywords = {Bayesian statistics,brms,isotonic regression,ordinal variables,R,Stan},
  language = {en}
}

@article{burknerOrdinalRegressionModels2019,
  title = {Ordinal Regression Models in Psychology: {{A}} Tutorial},
  shorttitle = {Ordinal {{Regression Models}} in {{Psychology}}},
  author = {B{\"u}rkner, Paul-Christian and Vuorre, Matti},
  year = {2019},
  month = mar,
  volume = {2},
  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.},
  journal = {Advances in Methods and Practices in Psychological Science},
  language = {en},
  number = {1}
}

@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},
  year = {2017},
  volume = {76},
  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},
  journal = {Journal of statistical software},
  number = {1}
}

@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},
  year = {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.},
  year = {1992},
  month = aug,
  volume = {46},
  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.},
  file = {/Users/solomonkurz/Zotero/storage/7G3SEDKK/Casella and George - 1992 - Explaining the Gibbs Sampler.pdf;/Users/solomonkurz/Zotero/storage/SFZUD4XZ/00031305.1992.html},
  journal = {The American Statistician},
  keywords = {Data augmentation,Markov chains,Monte Carlo methods,Resampling techniques},
  number = {3}
}

@book{cover2006elements,
  title = {Elements of Information Theory},
  author = {Cover, Thomas M and Thomas, Joy A},
  year = {2006},
  edition = {2nd Edition},
  publisher = {{John Wiley \& Sons}},
  url = {https://www.wiley.com/en-us/Elements+of+Information+Theory\%2C+2nd+Edition-p-9780471241959},
  isbn = {978-0-471-24195-9}
}

@article{cummingNewStatisticsWhy2014,
  title = {The New Statistics: {{Why}} and How},
  shorttitle = {The {{New Statistics}}},
  author = {Cumming, Geoff},
  year = {2014},
  month = jan,
  volume = {25},
  pages = {7--29},
  publisher = {{SAGE Publications Inc}},
  issn = {0956-7976},
  doi = {10.1177/0956797613504966},
  url = {https://journals.sagepub.com/doi/pdf/10.1177/0956797613504966},
  urldate = {2020-05-21},
  abstract = {We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.},
  file = {/Users/solomonkurz/Zotero/storage/UJMRBZGC/Cumming - 2014 - The New Statistics Why and How.pdf},
  journal = {Psychological Science},
  number = {1}
}

@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},
  year = {2006},
  month = dec,
  volume = {17},
  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.},
  journal = {Psychological Science},
  language = {en},
  number = {12}
}

@article{derooijCrossvalidationMethodEvery2020,
  title = {Cross-Validation: {{A}} Method Every Psychologist Should Know},
  shorttitle = {Cross-{{Validation}}},
  author = {{de Rooij}, Mark and Weeda, Wouter},
  year = {2020},
  month = may,
  volume = {3},
  pages = {248--263},
  publisher = {{SAGE Publications Inc}},
  issn = {2515-2459},
  doi = {10.1177/2515245919898466},
  url = {https://doi.org/10.1177/2515245919898466},
  urldate = {2020-06-03},
  abstract = {Cross-validation is a statistical procedure that every psychologist should know. Most are possibly familiar with the procedure in a global way but have not used it for the analysis of their own data. We introduce cross-validation for the purpose of model selection in a general sense, as well as an R package we have developed for this kind of analysis, and we present examples illustrating the use of this package for types of research problems that are often encountered in the social sciences. Cross-validation can be an easy-to-use alternative to null-hypothesis testing, and it has the benefit that it does not make as many assumptions.},
  file = {/Users/solomonkurz/Zotero/storage/S7SBFDUC/de Rooij and Weeda - 2020 - Cross-Validation A Method Every Psychologist Shou.pdf},
  journal = {Advances in Methods and Practices in Psychological Science},
  language = {en},
  number = {2}
}

@book{dunn2018generalized,
  title = {Generalized Linear Models with Examples in {{R}}},
  author = {Dunn, Peter K and Smyth, Gordon K},
  year = {2018},
  publisher = {{Springer}},
  url = {https://link.springer.com/book/10.1007/978-1-4419-0118-7}
}

@article{efronSteinParadoxStatistics1977,
  title = {Stein's Paradox in Statistics},
  author = {Efron, Bradley and Morris, Carl},
  year = {1977},
  volume = {236},
  pages = {119--127},
  publisher = {{Scientific American, a division of Nature America, Inc.}},
  issn = {0036-8733},
  doi = {10.1038/scientificamerican0577-119},
  url = {https://www.jstor.org/stable/24954030},
  urldate = {2020-05-17},
  journal = {Scientific American},
  number = {5}
}

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

@article{gabryPlottingMCMCDraws2019,
  title = {Plotting {{MCMC}} Draws Using the Bayesplot Package},
  author = {Gabry, Jonah},
  year = {2019},
  month = nov,
  url = {https://CRAN.R-project.org/package=bayesplot/vignettes/plotting-mcmc-draws.html},
  urldate = {2020-05-26},
  language = {English}
}

@article{gabryVisualMCMCDiagnostics2020,
  title = {Visual {{MCMC}} Diagnostics Using the Bayesplot Package},
  author = {Gabry, Jonah and Modr{\'a}k, Martin},
  year = {2020},
  month = may,
  url = {https://CRAN.R-project.org/package=bayesplot/vignettes/plotting-mcmc-draws.html},
  urldate = {2020-06-11},
  language = {English}
}

@article{gelman2006difference,
  title = {The Difference between ``Significant'' and ``Not Significant'' Is Not Itself Statistically Significant},
  author = {Gelman, Andrew and Stern, Hal},
  year = {2006},
  volume = {60},
  pages = {328--331},
  publisher = {{Taylor \& Francis}},
  doi = {10.1198/000313006X152649},
  url = {https://www.tandfonline.com/doi/pdf/10.1198/000313006X152649?needAccess=true},
  journal = {The American Statistician},
  number = {4}
}

@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},
  year = {2013},
  publisher = {{CRC press}},
  url = {https://stat.columbia.edu/~gelman/book/}
}

@article{gelmanAreConfidenceIntervals2019,
  title = {Are Confidence Intervals Better Termed ``Uncertainty Intervals''?},
  author = {Gelman, Andrew and Greenland, Sander},
  year = {2019},
  month = sep,
  pages = {l5381},
  issn = {0959-8138, 1756-1833},
  doi = {10.1136/bmj.l5381},
  url = {https://stat.columbia.edu/~gelman/research/published/uncertainty_intervals.pdf},
  urldate = {2020-05-21},
  file = {/Users/solomonkurz/Zotero/storage/TVDUC9Z3/Gelman and Greenland - 2019 - Are confidence intervals better termed “uncertaint.pdf},
  journal = {BMJ},
  language = {en}
}

@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},
  year = {2013},
  month = nov,
  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.},
  file = {/Users/solomonkurz/Zotero/storage/EA32DKC7/Gelman and Loken - The garden of forking paths Why multiple comparis.pdf},
  language = {en}
}

@article{gelmanPostratificationManyCategories1997,
  title = {Postratification into Many Categories Using Hierarchical Logistic Regression},
  author = {Gelman, Andrew and Little, Thomas C.},
  year = {1997},
  month = sep,
  volume = {23},
  pages = {127--135},
  url = {https://stat.columbia.edu/~gelman/research/published/poststrat3.pdf},
  journal = {Survey Methodology},
  language = {English}
}

@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},
  year = {2017},
  month = oct,
  volume = {19},
  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.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  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},
  journal = {Entropy},
  keywords = {Bayesian inference,default priors,prior distribution},
  language = {en},
  number = {10}
}

@article{gelmanRsquaredBayesianRegression2019,
  title = {R-Squared for {{Bayesian}} Regression Models},
  author = {Gelman, Andrew and Goodrich, Ben and Gabry, Jonah and Vehtari, Aki},
  year = {2019},
  month = jul,
  volume = {73},
  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},
  journal = {The American Statistician},
  language = {en},
  number = {3}
}

@article{gelmanWhyHighorderPolynomials2019,
  title = {Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs},
  author = {Gelman, Andrew and Imbens, Guido},
  year = {2019},
  month = jul,
  volume = {37},
  pages = {447--456},
  publisher = {{Taylor \& Francis}},
  issn = {0735-0015},
  doi = {10.1080/07350015.2017.1366909},
  url = {https://amstat.tandfonline.com/doi/full/10.1080/07350015.2017.1366909},
  urldate = {2020-07-30},
  abstract = {It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of the polynomial, and poor coverage of confidence intervals. We recommend researchers instead use estimators based on local linear or quadratic polynomials or other smooth functions.},
  file = {/Users/solomonkurz/Zotero/storage/IZ6XLLYA/Gelman and Imbens - 2019 - Why High-Order Polynomials Should Not Be Used in R.pdf;/Users/solomonkurz/Zotero/storage/PK67RHKK/07350015.2017.html},
  journal = {Journal of Business \& Economic Statistics},
  number = {3}
}

@article{gemanStochasticRelaxationGibbs1984,
  title = {Stochastic Relaxation, {{Gibbs}} Distributions, and the {{Bayesian}} Restoration of Images},
  author = {Geman, Stuart and Geman, Donald},
  year = {1984},
  month = nov,
  volume = {PAMI-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.},
  file = {/Users/solomonkurz/Zotero/storage/M4USX4TH/4767596.html},
  journal = {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},
  number = {6}
}

@book{grafenModernStatisticsLife2002,
  title = {Modern Statistics for the Life Sciences},
  author = {Grafen, Alan and Hails, Rosie},
  year = {2002},
  month = may,
  publisher = {{Oxford University Press}},
  address = {{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.},
  file = {/Users/solomonkurz/Zotero/storage/9CLZ5C5J/modern-statistics-for-the-life-sciences-9780199252312.html},
  isbn = {978-0-19-925231-2}
}

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

@misc{HadleyPrecisSource,
  title = {Hadley/Precis Source: {{R}}/Histospark.{{R}}},
  shorttitle = {Hadley/Precis Source},
  url = {https://rdrr.io/github/hadley/precis/src/R/histospark.R},
  urldate = {2020-05-22},
  abstract = {R/histospark.R defines the following functions:},
  file = {/Users/solomonkurz/Zotero/storage/WDBQG93J/histospark.html},
  language = {en}
}

@book{hastie2009elements,
  title = {The Elements of Statistical Learning: Data Mining, Inference, and Prediction},
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
  year = {2009},
  publisher = {{Springer Science \& Business Media}},
  doi = {10.1007/978-0-387-84858-7},
  url = {https://link.springer.com/book/10.1007\%2F978-0-387-84858-7}
}

@article{hauerHarmDoneTests2004,
  title = {The Harm Done by Tests of Significance},
  author = {Hauer, Ezra},
  year = {2004},
  month = may,
  volume = {36},
  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.},
  file = {/Users/solomonkurz/Zotero/storage/Y8LYGNMT/Hauer - 2004 - The harm done by tests of significance.pdf},
  journal = {Accident Analysis \& Prevention},
  language = {en},
  number = {3}
}

@book{hayes2017introduction,
  title = {Introduction to Mediation, Moderation, and Conditional Process Analysis: {{A}} Regression-Based Approach},
  author = {Hayes, Andrew F},
  year = {2017},
  publisher = {{Guilford publications}},
  url = {https://www.guilford.com/books/Introduction-to-Mediation-Moderation-and-Conditional-Process-Analysis/Andrew-Hayes/9781462534654},
  isbn = {978-1-4625-3465-4}
}

@article{hedekerApplicationMixedeffectsLocation2008,
  title = {An Application of a Mixed-Effects Location Scale Model for Analysis of Ecological Momentary Assessment ({{EMA}}) Data},
  author = {Hedeker, Donald and Mermelstein, Robin J. and Demirtas, Hakan},
  year = {2008},
  month = jun,
  volume = {64},
  pages = {627--634},
  issn = {0006-341X},
  doi = {10.1111/j.1541-0420.2007.00924.x},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2424261/},
  urldate = {2020-08-05},
  abstract = {For longitudinal data, mixed models include random subject effects to indicate how subjects influence their responses over repeated assessments. The error variance and the variance of the random effects are usually considered to be homogeneous. These variance terms characterize the within-subjects (i.e., error variance) and between-subjects (i.e., random-effects variance) variation in the data. In studies using ecological momentary assessment (EMA), up to 30 or 40 observations are often obtained for each subject, and interest frequently centers around changes in the variances, both within and between subjects. In this article, we focus on an adolescent smoking study using EMA where interest is on characterizing changes in mood variation. We describe how covariates can influence the mood variances, and also extend the standard mixed model by adding a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of their mood responses. Additionally, we allow the location and scale random effects to be correlated. These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.},
  file = {/Users/solomonkurz/Zotero/storage/WV9YLG3T/Hedeker et al. - 2008 - An Application of a Mixed-Effects Location Scale M.pdf},
  journal = {Biometrics},
  number = {2},
  pmcid = {PMC2424261},
  pmid = {17970819}
}

@article{hedekerModelingWithinsubjectVariance2012,
  title = {Modeling Between- and within-Subject Variance in Ecological Momentary Assessment ({{EMA}}) Data Using Mixed-Effects Location Scale Models},
  author = {Hedeker, Donald and Mermelstein, Robin J. and Demirtas, Hakan},
  year = {2012},
  month = nov,
  volume = {31},
  issn = {0277-6715},
  doi = {10.1002/sim.5338},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655706/},
  urldate = {2020-08-05},
  abstract = {Ecological Momentary Assessment (EMA) and/or Experience Sampling (ESM) methods are increasingly used in health studies to study subjective experiences within changing environmental contexts. In these studies, up to thirty or forty observations are often obtained for each subject. Because there are so many measurements per subject, one can characterize a subject's mean and variance, and specify models for both. In this article, we focus on an adolescent smoking study using EMA where interest is on characterizing changes in mood variation. We describe how covariates can influence the mood variances, and also extend the statistical model by adding a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of their mood responses. These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.},
  file = {/Users/solomonkurz/Zotero/storage/62M7KKN5/Hedeker et al. - 2012 - Modeling Between- and Within-Subject Variance in E.pdf},
  journal = {Statistics in medicine},
  number = {27},
  pmcid = {PMC3655706},
  pmid = {22419604}
}

@article{hindePrimateMilkProximate2011,
  title = {Primate Milk: {{Proximate}} Mechanisms and Ultimate Perspectives},
  shorttitle = {Primate Milk},
  author = {Hinde, Katie and Milligan, Lauren A.},
  year = {2011},
  volume = {20},
  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.},
  copyright = {Copyright \textcopyright{} 2011 Wiley Periodicals, Inc.},
  file = {/Users/solomonkurz/Zotero/storage/7YIU3Z5X/evan.html},
  journal = {Evolutionary Anthropology: Issues, News, and Reviews},
  keywords = {infant development,lactation,life history,maternal investment,reproductive ecology},
  language = {en},
  number = {1}
}

@book{hoffmanLongitudinalAnalysisModeling2015,
  title = {Longitudinal Analysis: {{Modeling}} within-Person Fluctuation and Change},
  author = {Hoffman, Lesa},
  year = {2015},
  edition = {1 edition},
  publisher = {{Routledge}},
  address = {{New York}},
  url = {https://www.routledge.com/Longitudinal-Analysis-Modeling-Within-Person-Fluctuation-and-Change/Hoffman/p/book/9780415876025},
  abstract = {Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter.  Written for non-mathematically-oriented readers, this text features:   A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data   An emphasis on how the terminology, interpretation, and estimation of familiar general linear models relates to those of more complex models for longitudinal data    Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers' understanding of the overall model-building process   Sample results sections for each example to provide useful templates for published reports   Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own data The book opens with the building blocks of longitudinal analysis\rule{1em}{1pt}general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data.  Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book's accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.},
  isbn = {978-0-415-87602-5},
  language = {English}
}

@book{howell2001demography,
  title = {Demography of the Dobe! {{Kung}}},
  author = {Howell, Nancy},
  year = {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},
  year = {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{kayExtractingVisualizingTidy2020,
  title = {Extracting and Visualizing Tidy Draws from Brms Models},
  author = {Kay, Matthew},
  year = {2020},
  month = apr,
  url = {https://mjskay.github.io/tidybayes/articles/tidy-brms.html},
  urldate = {2020-05-17},
  abstract = {tidybayes},
  file = {/Users/solomonkurz/Zotero/storage/NT83AM3T/tidy-brms.html},
  language = {en}
}

@misc{kayMarginalDistributionSingle2020,
  title = {Marginal Distribution of a Single Correlation from an {{LKJ}} Distribution},
  author = {Kay, Matthew},
  year = {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.},
  file = {/Users/solomonkurz/Zotero/storage/2TMG4ARE/lkjcorr_marginal.html},
  language = {en}
}

@article{kennedyKnowYourPopulation2020,
  title = {Know Your Population and Know Your Model: {{Using}} Model-Based Regression and Poststratification to Generalize Findings beyond the Observed Sample},
  shorttitle = {Know Your Population and Know Your Model},
  author = {Kennedy, Lauren and Gelman, Andrew},
  year = {2020},
  month = apr,
  url = {http://arxiv.org/abs/1906.11323},
  urldate = {2020-07-28},
  abstract = {Psychology research focuses on interactions, and this has deep implications for inference from non-representative samples. For the goal of estimating average treatment effects, we propose to fit a model allowing treatment to interact with background variables and then average over the distribution of these variables in the population. This can be seen as an extension of multilevel regression and poststratification (MRP), a method used in political science and other areas of survey research, where researchers wish to generalize from a sparse and possibly non-representative sample to the general population. In this paper, we discuss areas where this method can be used in the psychological sciences. We use our method to estimate the norming distribution for the Big Five Personality Scale using open source data. We argue that large open data sources like this and other collaborative data sources can be combined with MRP to help resolve current challenges of generalizability and replication in psychology.},
  archivePrefix = {arXiv},
  eprint = {1906.11323},
  eprinttype = {arxiv},
  file = {/Users/solomonkurz/Zotero/storage/6HFKVVBX/Kennedy and Gelman - 2020 - Know your population and know your model Using mo.pdf;/Users/solomonkurz/Zotero/storage/8HSAKEKD/1906.html},
  journal = {arXiv:1906.11323 [stat]},
  keywords = {Statistics - Applications},
  primaryClass = {stat}
}

@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},
  year = {2013},
  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.},
  file = {/Users/solomonkurz/Zotero/storage/2DI5JTLT/Kievit et al. - 2013 - Simpson's paradox in psychological science a prac.pdf},
  journal = {Frontiers in Psychology},
  keywords = {ecological fallacy,Measurement,Paradox,Reductionism,simpson's paradox,statistical inference},
  language = {English}
}

@article{klinePopulationSizePredicts2010,
  title = {Population Size Predicts Technological Complexity in {{Oceania}}},
  author = {Kline, Michelle A. and Boyd, Robert},
  year = {2010},
  month = aug,
  volume = {277},
  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},
  journal = {Proceedings of the Royal Society B: Biological Sciences},
  number = {1693}
}

@article{kosterFoodSharingNetworks2014,
  title = {Food Sharing Networks in Lowland {{Nicaragua}}: {{An}} Application of the Social Relations Model to Count Data},
  shorttitle = {Food Sharing Networks in Lowland {{Nicaragua}}},
  author = {Koster, Jeremy M. and Leckie, George},
  year = {2014},
  month = jul,
  volume = {38},
  pages = {100--110},
  issn = {0378-8733},
  doi = {10.1016/j.socnet.2014.02.002},
  url = {https://www.researchgate.net/profile/Jeremy_Koster/publication/261764179_Food_sharing_networks_in_lowland_Nicaragua_An_application_of_the_social_relations_model_to_count_data/links/5c413437299bf12be3d04539/Food-sharing-networks-in-lowland-Nicaragua-An-application-of-the-social-relations-model-to-count-data.pdf},
  urldate = {2020-08-01},
  abstract = {Previous research on food sharing in small-scale societies provides support for multiple evolutionary hypotheses, but evolutionary anthropologists have devoted relatively little attention to the broader relational context of inter-household transfers of food. The present research observes transfers of meat over a yearlong period among 25 households of indigenous Mayangna and Miskito horticulturalists in Nicaragua. To analyze these data, we extend the multilevel formulation of the social relations model to count data, namely the number of portions of meat exchanged between households. Along with other covariates, we examine the effect of an ``association index,'' which reflects the amount of time that households interact with one another. The association index exhibits a positive effect on sharing, and our overall results indicate that food sharing networks largely correspond to kin-based networks of social interaction, suggesting that food sharing is embedded in broader social relationships between households. We discuss possible extensions of our methodological approach, as appropriate for research on food sharing and social network analysis more broadly.},
  file = {/Users/solomonkurz/Zotero/storage/4KUGED2E/Koster and Leckie - 2014 - Food sharing networks in lowland Nicaragua An app.pdf;/Users/solomonkurz/Zotero/storage/H6X59WZN/S0378873314000148.html},
  journal = {Social Networks},
  keywords = {Association networks,Behavioral ecology,Cooperation,Count data,Multilevel model,Social relations model},
  language = {en}
}

@book{kruschkeDoingBayesianData2015,
  title = {Doing {{Bayesian}} Data Analysis: {{A}} Tutorial with {{R}}, {{JAGS}}, and {{Stan}}},
  author = {Kruschke, John K.},
  year = {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.},
  year = {1951},
  month = mar,
  volume = {22},
  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},
  file = {/Users/solomonkurz/Zotero/storage/ZRSE9FMG/Kullback and Leibler - 1951 - On Information and Sufficiency.pdf;/Users/solomonkurz/Zotero/storage/QVXJE6S9/1177729694.html},
  journal = {Annals of Mathematical Statistics},
  language = {EN},
  mrnumber = {MR39968},
  number = {1},
  zmnumber = {0042.38403}
}

@book{kurzDoingBayesianData2020,
  title = {Doing {{Bayesian}} Data Analysis in Brms and the Tidyverse},
  author = {Kurz, A. Solomon},
  year = {2020},
  month = may,
  edition = {version 0.2.0},
  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{kurzRecodingIntroductionMediation2019,
  title = {Recoding {{Introduction}} to Mediation, Moderation, and Conditional Process Analysis},
  author = {Kurz, A. Solomon},
  year = {2019},
  month = dec,
  edition = {version 1.1.0},
  doi = {10.5281/zenodo.3589999},
  url = {https://bookdown.org/ajkurz/recoding_Hayes_2018/},
  urldate = {2020-06-10},
  abstract = {This project is an effort to connect his Hayes's conditional process analysis work with the Bayesian paradigm. Herein I refit his models with my favorite R package for Bayesian regression, B\"urkner's brms, and use the tidyverse for data manipulation and plotting.},
  file = {/Users/solomonkurz/Zotero/storage/IKVQT47J/recoding_Hayes_2018.html}
}

@book{kurzStatisticalRethinkingBrms2020,
  title = {Statistical Rethinking with Brms, Ggplot2, and the Tidyverse},
  author = {Kurz, A. Solomon},
  year = {2020},
  month = mar,
  edition = {version 1.1.0},
  doi = {10.5281/zenodo.3693202},
  url = {https://bookdown.org/content/3890/},
  urldate = {2020-05-16},
  abstract = {This project is an attempt to re-express the code in McElreath's 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/MTCXZRHZ/3890.html}
}

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

@misc{LongitudinalAnalysisModeling,
  title = {Longitudinal {{Analysis}}: {{Modeling Within}}-{{Person Fluctuation}} and {{Change}}},
  shorttitle = {Longitudinal {{Analysis}}},
  url = {https://www.routledge.com/Longitudinal-Analysis-Modeling-Within-Person-Fluctuation-and-Change/Hoffman/p/book/9780415876025},
  urldate = {2020-08-06},
  abstract = {Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Writt},
  journal = {Routledge \& CRC Press},
  language = {en}
}

@article{marinGgmcmcAnalysisMCMC2016,
  title = {{{ggmcmc}}: {{Analysis}} of {{MCMC}} Samples and {{Bayesian}} Inference},
  author = {i Mar{\'i}n, Xavier Fern{\'a}ndez},
  year = {2016},
  volume = {70},
  pages = {1--20},
  doi = {10.18637/jss.v070.i09},
  journal = {Journal of Statistical Software},
  number = {9}
}

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

@book{mcelreathStatisticalRethinkingBayesian2015,
  title = {Statistical Rethinking: {{A Bayesian}} Course with Examples in {{R}} and {{Stan}}},
  author = {McElreath, Richard},
  year = {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},
  year = {2020},
  month = mar,
  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},
  keywords = {Mathematics / Probability \& Statistics / General},
  language = {en}
}

@article{meehlWhySummariesResearch1990,
  title = {Why Summaries of Research on Psychological Theories Are Often Uninterpretable},
  author = {Meehl, Paul E.},
  year = {1990},
  month = feb,
  volume = {66},
  pages = {195--244},
  publisher = {{SAGE Publications Inc}},
  issn = {0033-2941},
  doi = {10.2466/pr0.1990.66.1.195},
  url = {http://meehl.umn.edu/sites/meehl.dl.umn.edu/files/144whysummaries.pdf},
  urldate = {2020-05-25},
  abstract = {Null hypothesis testing of correlational predictions from weak substantive theories in soft psychology is subject to the influence of ten obfuscating factors whose effects are usually (1) sizeable, (2) opposed, (3) variable, and (4) unknown. The net epistemic effect of these ten obfuscating influences is that the usual research literature review is well-nigh uninterpretable. Major changes in graduate education, conduct of research, and editorial policy are proposed.},
  file = {/Users/solomonkurz/Zotero/storage/AYF7PQVK/Meehl - 1990 - Why Summaries of Research on Psychological Theorie.pdf},
  journal = {Psychological Reports},
  language = {en},
  number = {1}
}

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

@article{mgcv2003,
  title = {Thin-Plate Regression Splines},
  author = {Wood, Simon N},
  year = {2003},
  volume = {65},
  pages = {95--114},
  doi = {10.1111/1467-9868.00374},
  url = {https://s3.amazonaws.com/academia.edu.documents/50461038/1467-9868.0037420161121-28486-yjewi1.pdf?response-content-disposition=inline\%3B\%20filename\%3DThin_plate_regression_splines.pdf\&X-Amz-Algorithm=AWS4-HMAC-SHA256\&X-Amz-Credential=ASIATUSBJ6BANKOPOZEO\%2F20200524\%2Fus-east-1\%2Fs3\%2Faws4_request\&X-Amz-Date=20200524T180240Z\&X-Amz-Expires=3600\&X-Amz-SignedHeaders=host\&X-Amz-Security-Token=IQoJb3JpZ2luX2VjECEaCXVzLWVhc3QtMSJIMEYCIQDOX\%2FwTEMlLub97I\%2FMAMW8be650dvGXcsUS\%2FFT7fRLTFAIhALm6fierGHEIpDpqKXIzaF7X4n0Vgz6oeQya42qaxCUbKrQDCHkQABoMMjUwMzE4ODExMjAwIgz0HouvSIxUl554eAEqkQMCsNlb\%2BQSuS55lUhyF7eBFSq665pRSMi4DyFt0hNfLRi6gKXYUyv0Cqgog7u9dhX1rpMWmmOcD9bIZCqWNHirMlqynxY7K5\%2F5\%2BCosSEzqlDE5DA2oOH4hqgemL24qvpdh\%2F7sB57yZu5O4zlmsFHotrhhSCkMzk5JR3kvuC4BRjaI055JWQR\%2F\%2Bofp\%2Fuc\%2FlD\%2FGnQ8gUHBcw847tQyUPEZvPg0\%2Frv68j3I0jWjy2kon7Jgq5GJqjpD15aIQlUcLicQ0adoxfirnV3wS14m7\%2FNr0fdddpmOpJ\%2BPDlwyqGY6PN3oo9XZYUShrRkhzaOC8aERDg0N5WbWrGIMr0nFmoM4xZqBuxob1iHyTfpmrtN14YBEbGgVRPbCjc9YxwrPe7sr\%2B\%2Ff5\%2F71aNZfNW22td0HLuUiijGW1gKyKqEE907O3oDim0QD\%2B38EmW7N0apRBqZyE4oQA440ypSXABqa7\%2FkwspRZnBQVisqdMETD4m7W9jqTYr2OONaPJMTg\%2F2951ZbnQB87aczCx6fbb7K3snnOsQmHRDDRuKr2BTrqATfPzy8Sxe79\%2BEP54Yvh\%2FVuK9bF8SxInrfmJ1nITVtm3mhhleH9\%2BmbumR0HxkiPVqEXAq2C6wI5Akj6I3zXVzBFFnNzgPA\%2BHvWcUWBH33yJ4nmhZ8dDNMruPQ0H9lmya2noeaOr0w\%2FWS3VgLpkC\%2F\%2FCAjaFQydz1WZp1onsbpi66aeLxgsIecPfqD\%2FocvtDACzZxw3JSM7G\%2BSgSpcGdRvouHivT93d8hYmjLVYTXPR2J6BqwMC\%2BNXy\%2F4jBQcvWl08pPA0FjGdoNcestAXGwNkhsWv6RvMYo7rCpR5iyTlBkk5dNRz\%2FSl7wT37hA\%3D\%3D\&X-Amz-Signature=92b8be88b62ae556465c7e485d1f99917e86dad15ccaf9b0a7def69a5c716173},
  journal = {Journal of the Royal Statistical Society (B)},
  number = {1}
}

@article{mgcv2004,
  title = {Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models},
  author = {Wood, Simon N},
  year = {2004},
  volume = {99},
  pages = {673--686},
  doi = {10.1198/016214504000000980},
  url = {https://purehost.bath.ac.uk/ws/portalfiles/portal/9228711/magic.pdf},
  journal = {Journal of the American Statistical Association},
  number = {467}
}

@article{mgcv2011,
  title = {Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models},
  author = {Wood, Simon N},
  year = {2011},
  volume = {73},
  pages = {3--36},
  doi = {10.1111/j.1467-9868.2010.00749.x},
  url = {https://people.bath.ac.uk/man54/SAMBa/ITTs/ITT2/EDF/REMLWood2009.pdf},
  journal = {Journal of the Royal Statistical Society (B)},
  number = {1}
}

@article{mgcv2016,
  title = {Smoothing Parameter and Model Selection for General Smooth Models (with Discussion)},
  author = {Wood, Simon N and Pya, Natalya and S{\"a}fken, Benjamin},
  year = {2016},
  volume = {111},
  pages = {1548--1575},
  doi = {10.1080/01621459.2016.1180986},
  url = {https://doi.org/10.1080/01621459.2016.1180986},
  journal = {Journal of the American Statistical Association}
}

@book{mgcv2017,
  title = {Generalized Additive Models: {{An}} Introduction with r},
  author = {Wood, Simon N},
  year = {2017},
  edition = {Second},
  publisher = {{Chapman and Hall/CRC}},
  url = {https://www.routledge.com/Generalized-Additive-Models-An-Introduction-with-R-Second-Edition/Wood/p/book/9781498728331?utm_source=crcpress.com\&utm_medium=referral},
  isbn = {978-1-4987-2833-1}
}

@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.},
  year = {2019},
  month = mar,
  volume = {2},
  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.},
  file = {/Users/solomonkurz/Zotero/storage/3D6FMZVD/Navarro - 2019 - Between the Devil and the Deep Blue Sea Tensions .pdf},
  journal = {Computational Brain \& Behavior},
  language = {en},
  number = {1}
}

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

@article{parkBayesianMultilevelEstimation2004,
  title = {Bayesian Multilevel Estimation with Poststratification: {{State}}-Level Estimates from National Polls},
  shorttitle = {Bayesian {{Multilevel Estimation}} with {{Poststratification}}},
  author = {Park, David K. and Gelman, Andrew and Bafumi, Joseph},
  year = {2004},
  volume = {12},
  pages = {375--385},
  publisher = {{[Oxford University Press, Society for Political Methodology]}},
  issn = {1047-1987},
  url = {https://www.jstor.org/stable/25791784},
  urldate = {2020-07-27},
  abstract = {We fit a multilevel logistic regression model for the mean of a binary response variable conditional on poststratification cells. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification (see Gelman and Little 1997, Survey Methodology 23:127\textendash 135). To validate the method, we apply it to U.S. preelection polls for 1988 and 1992, poststratified by state, region, and the usual demographic variables. We evaluate the model by comparing it to state-level election outcomes. The multilevel model outperforms more commonly used models in political science. We envision the most important usage of this method to be not forecasting elections but estimating public opinion on a variety of issues at the state level.},
  journal = {Political Analysis},
  number = {4}
}

@book{pengMasteringSoftwareDevelopment2017,
  title = {Mastering Software Development in \{\vphantom\}{{R}}\vphantom\{\}},
  author = {Peng, Roger D. and Kross, Sean and Anderson, Brooke},
  year = {2017},
  month = sep,
  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.},
  year = {2019},
  url = {https://bookdown.org/rdpeng/rprogdatascience/}
}

@misc{PivotDataWide2020,
  title = {Pivot Data from Wide to Long \textemdash{} Pivot\_longer},
  year = {2020},
  url = {https://tidyr.tidyverse.org/reference/pivot_longer.html},
  urldate = {2020-05-21},
  abstract = {pivot\_longer() "lengthens" data, increasing the number of rows and
decreasing the number of columns. The inverse transformation is
pivot\_wider()
Learn more in vignette("pivot").},
  file = {/Users/solomonkurz/Zotero/storage/D62EQVQQ/pivot_longer.html},
  language = {en}
}

@misc{Pivoting2020,
  title = {Pivoting},
  year = {2020},
  url = {https://tidyr.tidyverse.org/articles/pivot.html},
  urldate = {2020-05-21},
  abstract = {Learn how use the new `pivot\_longer()` and `pivot\_wider()` functions which change the representation of a dataset without changing the data it contains.},
  file = {/Users/solomonkurz/Zotero/storage/AAYYSFTP/pivot.html},
  language = {en}
}

@article{plummerJAGSProgramAnalysis2003,
  title = {{{JAGS}}: {{A}} Program for Analysis of {{Bayesian}} Graphical Models Using {{Gibbs}} Sampling},
  author = {Plummer, Martyn},
  year = {2003},
  pages = {8},
  url = {http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Drafts/Plummer.pdf},
  abstract = {JAGS is a program for Bayesian Graphical modelling which aims for compatibility with Classic BUGS. The program could eventually be developed as an R package. This article explains the motivations for this program, briefly describes the architecture and then discusses some ideas for a vectorized form of the BUGS language.},
  file = {/Users/solomonkurz/Zotero/storage/UX2NECXA/Plummer - 2003 - JAGS A program for analysis of Bayesian graphical.pdf},
  journal = {Working Papers},
  language = {en}
}

@manual{R-ape,
  title = {{{ape}}: {{Analyses}} of Phylogenetics and Evolution},
  author = {Paradis, Emmanuel and Blomberg, Simon and Bolker, Ben and Brown, Joseph and Claramunt, Santiago and Claude, Julien and Cuong, Hoa Sien and Desper, Richard and Didier, Gilles and Durand, Benoit and Dutheil, Julien and Ewing, RJ and Gascuel, Olivier and Guillerme, Thomas and Heibl, Christoph and Ives, Anthony and Jones, Bradley and Krah, Franz and Lawson, Daniel and Lefort, Vincent and Legendre, Pierre and Lemon, Jim and Louvel, Guillaume and Marcon, Eric and McCloskey, Rosemary and Nylander, Johan and {Opgen-Rhein}, Rainer and Popescu, Andrei-Alin and {Royer-Carenzi}, Manuela and Schliep, Klaus and Strimmer, Korbinian and {de Vienne}, Damien},
  year = {2020},
  url = {https://CRAN.R-project.org/package=ape},
  type = {Manual}
}

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

@book{R-bayesplot,
  title = {{{bayesplot}}: {{Plotting}} for {{Bayesian}} Models},
  author = {Gabry, Jonah and Mahr, Tristan},
  year = {2019},
  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},
  year = {2020},
  url = {https://CRAN.R-project.org/package=blavaan}
}

@book{R-brms,
  title = {{{brms}}: {{Bayesian}} Regression Models Using '{{Stan}}'},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2020},
  url = {https://CRAN.R-project.org/package=brms}
}

@book{R-dagitty,
  title = {{{dagitty}}: {{Graphical}} Analysis of Structural Causal Models},
  author = {Textor, Johannes and van {der Zander}, Benito},
  year = {2016},
  url = {https://CRAN.R-project.org/package=dagitty}
}

@manual{R-dutchmasters,
  title = {{{dutchmasters}}},
  author = {Thoen, Edwin},
  year = {2019},
  url = {https://github.com/EdwinTh/dutchmasters},
  type = {Manual}
}

@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},
  year = {2020},
  url = {https://CRAN.R-project.org/package=GGally}
}

@book{R-ggdag,
  title = {{{ggdag}}: {{Analyze}} and Create Elegant Directed Acyclic Graphs},
  author = {Barrett, Malcolm},
  year = {2020},
  url = {https://CRAN.R-project.org/package=ggdag}
}

@book{R-ggmcmc,
  title = {{{ggmcmc}}: {{Tools}} for Analyzing {{MCMC}} Simulations from {{Bayesian}} Inference},
  author = {i Mar{\'i}n, Xavier Fern{\'a}ndez},
  year = {2020},
  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},
  year = {2020},
  url = {https://CRAN.R-project.org/package=ggplot2}
}

@manual{R-ggpomological,
  title = {{{ggpomological}}: {{Pomological}} Plot Theme for Ggplot2},
  author = {{Aden-Buie}, Garrick},
  year = {2020},
  url = {https://github.com/gadenbuie/ggpomological},
  type = {Manual}
}

@book{R-ggrepel,
  title = {{{ggrepel}}: {{Automatically}} Position Non-Overlapping Text Labels with 'Ggplot2'},
  author = {Slowikowski, Kamil},
  year = {2020},
  url = {https://CRAN.R-project.org/package=ggrepel}
}

@book{R-ggthemes,
  title = {{{ggthemes}}: {{Extra}} Themes, Scales and Geoms for 'Ggplot2'},
  author = {Arnold, Jeffrey B.},
  year = {2019},
  url = {https://CRAN.R-project.org/package=ggthemes}
}

@manual{R-ghibli,
  title = {{{ghibli}}: {{Studio}} Ghibli Colour Palettes},
  author = {Henderson, Ewen},
  year = {2020},
  url = {https://CRAN.R-project.org/package=ghibli},
  type = {Manual}
}

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

@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},
  year = {2019},
  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},
  year = {2019},
  url = {https://CRAN.R-project.org/package=MASS}
}

@book{R-mgcv,
  title = {{{mgcv}}: {{Mixed GAM}} Computation Vehicle with Automatic Smoothness Estimation},
  author = {Wood, Simon N},
  year = {2019},
  url = {https://CRAN.R-project.org/package=mgcv}
}

@book{R-patchwork,
  title = {{{patchwork}}: {{The}} Composer of Plots},
  author = {Pedersen, Thomas Lin},
  year = {2019},
  url = {https://CRAN.R-project.org/package=patchwork}
}

@book{R-posterior,
  title = {{{posterior}}: {{Tools}} for Working with Posterior Distributions},
  author = {B{\"u}rkner, Paul-Christian and Gabry, Jonah and Kay, Matthew and Vehtari, Aki},
  year = {2020},
  url = {https://mc-stan.org/posterior}
}

@book{R-psych,
  title = {{{psych}}: {{Procedures}} for Psychological, Psychometric, and Personality Research},
  author = {Revelle, William},
  year = {2020},
  url = {https://CRAN.R-project.org/package=psych}
}

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

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

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

@manual{R-rstanarm,
  title = {{{rstanarm}}: {{Bayesian}} Applied Regression Modeling via Stan},
  author = {Gabry, Jonah and Goodrich, Ben},
  year = {2020},
  url = {https://CRAN.R-project.org/package=rstanarm},
  type = {Manual}
}

@manual{R-statebins,
  title = {{{statebins}}: {{Create}} United States Uniform Cartogram Heatmaps},
  author = {Rudis, Bob},
  year = {2020},
  url = {https://CRAN.R-project.org/package=statebins},
  type = {Manual}
}

@book{R-tibble,
  title = {{{tibble}}: {{Simple}} Data Frames},
  author = {M{\"u}ller, Kirill and Wickham, Hadley},
  year = {2020},
  url = {https://CRAN.R-project.org/package=tibble}
}

@book{R-tidybayes,
  title = {{{tidybayes}}: {{Tidy}} Data and 'geoms' for {{Bayesian}} Models},
  author = {Kay, Matthew},
  year = {2020},
  url = {http://mjskay.github.io/tidybayes}
}

@book{R-tidyverse,
  title = {{{tidyverse}}: {{Easily}} Install and Load the 'Tidyverse'},
  author = {Wickham, Hadley},
  year = {2019},
  url = {https://CRAN.R-project.org/package=tidyverse}
}

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

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

@article{rastModelingIndividualDifferences2012,
  title = {Modeling Individual Differences in Within-Person Variation of Negative and Positive Affect in a Mixed Effects Location Scale Model Using {{BUGS}}/{{JAGS}}},
  author = {Rast, Philippe and Hofer, Scott M. and Sparks, Catharine},
  year = {2012},
  month = mar,
  volume = {47},
  pages = {177--200},
  publisher = {{Routledge}},
  issn = {0027-3171},
  doi = {10.1080/00273171.2012.658328},
  url = {https://doi.org/10.1080/00273171.2012.658328},
  urldate = {2020-08-05},
  abstract = {A mixed effects location scale model was used to model and explain individual differences in within-person variability of negative and positive affect across 7 days (N=178) within a measurement burst design. The data come from undergraduate university students and are pooled from a study that was repeated at two consecutive years. Individual differences in level and change in mood was modeled with a random intercept and random slope where the residual within-person variability was allowed to vary across participants. Additionally changes in within-person variability were explained by the inclusion of a time-varying predictor indicating the severity of daily stressors. This model accounted for 2 location and 2 scale effects and provided evidence that individuals who reported higher severity in daily stressors also exhibited greater variability in affect\textemdash but only for participants who showed low overall affect variability and who reported low average negative affect. Those who were more variable in their affect reports overall were less reactive to daily stressors in the sense that their high levels of affect variability remained high. We describe the utility of this model for further research on individual variation and change.},
  file = {/Users/solomonkurz/Zotero/storage/6C5DIABL/00273171.2012.html},
  journal = {Multivariate Behavioral Research},
  number = {2},
  pmid = {26734847}
}

@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},
  year = {2011},
  volume = {26},
  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.},
  journal = {Statistical Science},
  number = {1}
}

@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},
  year = {2018},
  url = {https://github.com/stan-dev/stancon_talks/}
}

@article{shannonMathematicalTheoryCommunication1948,
  title = {A Mathematical Theory of Communication},
  author = {Shannon, C. E.},
  year = {1948},
  volume = {27},
  pages = {379--423},
  issn = {1538-7305},
  doi = {10.1002/j.1538-7305.1948.tb01338.x},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/j.1538-7305.1948.tb01338.x},
  urldate = {2020-06-06},
  copyright = {\textcopyright{} 1948 The Bell System Technical Journal},
  file = {/Users/solomonkurz/Zotero/storage/2ZYC5TAW/Shannon - 1948 - A Mathematical Theory of Communication.pdf;/Users/solomonkurz/Zotero/storage/GJ7QEDI3/j.1538-7305.1948.tb01338.html},
  journal = {Bell System Technical Journal},
  language = {en},
  number = {3}
}

@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.},
  year = {2005},
  month = oct,
  volume = {437},
  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.},
  copyright = {2005 Nature Publishing Group},
  file = {/Users/solomonkurz/Zotero/storage/36JBUZ32/nature04243.html},
  journal = {Nature},
  language = {en},
  number = {7063}
}

@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},
  year = {2011},
  month = nov,
  volume = {22},
  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.},
  file = {/Users/solomonkurz/Zotero/storage/N85FNDMV/Simmons et al. - 2011 - False-Positive Psychology Undisclosed Flexibility.pdf},
  journal = {Psychological Science},
  language = {en},
  number = {11}
}

@book{singerAppliedLongitudinalData2003,
  title = {Applied Longitudinal Data Analysis: {{Modeling}} Change and Event Occurrence},
  shorttitle = {Applied Longitudinal Data Analysis},
  author = {Singer, Judith D. and Willett, John B.},
  year = {2003},
  month = mar,
  publisher = {{Oxford University Press, USA}},
  url = {https://oxford.universitypressscholarship.com/view/10.1093/acprof:oso/9780195152968.001.0001/acprof-9780195152968},
  abstract = {Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models.Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods.Visit http://www.ats.ucla.edu/stat/examples/alda.htm for:DT Downloadable data setsDT Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and moreDT Additional material for data analysis},
  googlebooks = {PpnA1M8VwR8C},
  isbn = {978-0-19-515296-8},
  keywords = {Mathematics / Probability \& Statistics / General,Medical / Epidemiology,Psychology / Statistics},
  language = {en}
}

@article{spiegelhalterBayesianMeasuresModel2002,
  title = {Bayesian Measures of Model Complexity and Fit},
  author = {Spiegelhalter, David J. and Best, Nicola G. and Carlin, Bradley P. and Linde, Angelika Van Der},
  year = {2002},
  volume = {64},
  pages = {583--639},
  issn = {1467-9868},
  doi = {10.1111/1467-9868.00353},
  url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00353},
  urldate = {2020-06-12},
  abstract = {Summary. We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which in normal models is the trace of the `hat' matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding pD to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.},
  file = {/Users/solomonkurz/Zotero/storage/GGPTKT9M/Spiegelhalter et al. - 2002 - Bayesian measures of model complexity and fit.pdf;/Users/solomonkurz/Zotero/storage/SD8PUJGW/1467-9868.html},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  keywords = {Bayesian model comparison,Decision theory,Deviance information criterion,Effective number of parameters,Hierarchical models,Information theory,Leverage,Markov chain Monte Carlo methods,Model dimension},
  language = {en},
  number = {4}
}

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

@book{standevelopmentteamStanFunctionsReference2020,
  title = {Stan Functions Reference},
  author = {{Stan Development Team}},
  year = {2020},
  url = {https://mc-stan.org/docs/2_23/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{standevelopmentteamStanReferenceManual2020,
  title = {Stan Reference Manual, {{Version}} 2.23},
  author = {{Stan Development Team}},
  year = {2020},
  url = {https://mc-stan.org/docs/2_23/reference-manual/}
}

@book{standevelopmentteamStanUserGuide2020,
  title = {Stan User's Guide, {{Version}} 2.23},
  author = {{Stan Development Team}},
  year = {2020},
  url = {https://mc-stan.org/docs/2_23/stan-users-guide/index.html}
}

@misc{SteinParadoxStatistics,
  title = {Stein's {{Paradox}} in {{Statistics}} on {{JSTOR}}},
  url = {https://www.jstor.org/preview-page/10.2307/24954030?seq=1},
  urldate = {2020-07-01},
  abstract = {Bradley Efron, Carl Morris, Stein's Paradox in Statistics, Scientific American, Vol. 236, No. 5 (May 1977), pp. 119-127},
  file = {/Users/solomonkurz/Zotero/storage/NVTSYVC3/24954030.html},
  language = {en}
}

@article{streetCoevolutionCulturalIntelligence2017,
  title = {Coevolution of Cultural Intelligence, Extended Life History, Sociality, and Brain Size in Primates},
  author = {Street, Sally E. and Navarrete, Ana F. and Reader, Simon M. and Laland, Kevin N.},
  year = {2017},
  month = jul,
  volume = {114},
  pages = {7908--7914},
  publisher = {{National Academy of Sciences}},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1620734114},
  url = {https://www.pnas.org/content/114/30/7908},
  urldate = {2020-08-03},
  abstract = {Explanations for primate brain expansion and the evolution of human cognition and culture remain contentious despite extensive research. While multiple comparative analyses have investigated variation in brain size across primate species, very few have addressed why primates vary in how much they use social learning. Here, we evaluate the hypothesis that the enhanced reliance on socially transmitted behavior observed in some primates has coevolved with enlarged brains, complex sociality, and extended lifespans. Using recently developed phylogenetic comparative methods we show that, across primate species, a measure of social learning proclivity increases with absolute and relative brain volume, longevity (specifically reproductive lifespan), and social group size, correcting for research effort. We also confirm relationships of absolute and relative brain volume with longevity (both juvenile period and reproductive lifespan) and social group size, although longevity is generally the stronger predictor. Relationships between social learning, brain volume, and longevity remain when controlling for maternal investment and are therefore not simply explained as a by-product of the generally slower life history expected for larger brained species. Our findings suggest that both brain expansion and high reliance on culturally transmitted behavior coevolved with sociality and extended lifespan in primates. This coevolution is consistent with the hypothesis that the evolution of large brains, sociality, and long lifespans has promoted reliance on culture, with reliance on culture in turn driving further increases in brain volume, cognitive abilities, and lifespans in some primate lineages.},
  chapter = {Colloquium Paper},
  copyright = {\textcopyright{}  . http://www.pnas.org/site/misc/userlicense.xhtml},
  file = {/Users/solomonkurz/Zotero/storage/UKCMQ4BS/Street et al. - 2017 - Coevolution of cultural intelligence, extended lif.pdf;/Users/solomonkurz/Zotero/storage/QREEYJJF/7908.html},
  journal = {Proceedings of the National Academy of Sciences},
  keywords = {brain evolution,cultural evolution,phylogenetic comparative analysis,primates,social learning},
  language = {en},
  number = {30},
  pmid = {28739950}
}

@article{subramanianAverageTreatmentEffect2018,
  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.},
  year = {2018},
  month = aug,
  volume = {210},
  pages = {77--82},
  issn = {1873-5347},
  doi = {10.1016/j.socscimed.2018.04.027},
  journal = {Social Science \& Medicine (1982)},
  keywords = {Randomized Controlled Trials as Topic,Retirement},
  language = {eng},
  pmid = {29724462}
}

@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.},
  year = {2018},
  month = aug,
  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},
  file = {/Users/solomonkurz/Zotero/storage/KB9NJ647/S0277953618301941.html},
  journal = {Social Science \& Medicine},
  language = {en},
  series = {Randomized {{Controlled Trials}} and {{Evidence}}-Based {{Policy}}: {{A Multidisciplinary Dialogue}}}
}

@book{tufteVisualDisplayQuantitative2001,
  title = {The Visual Display of Quantitative Information},
  author = {Tufte, Edward R.},
  year = {2001},
  month = may,
  edition = {2nd edition edition},
  publisher = {{Graphics Press}},
  address = {{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},
  language = {English}
}

@article{vehtariPracticalBayesianModel2017,
  title = {Practical {{Bayesian}} Model Evaluation Using Leave-One-out Cross-Validation and {{WAIC}}},
  author = {Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
  year = {2017},
  month = sep,
  volume = {27},
  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.},
  file = {/Users/solomonkurz/Zotero/storage/I7HY567V/Vehtari et al. - 2017 - Practical Bayesian model evaluation using leave-on.pdf},
  journal = {Statistics and Computing},
  language = {en},
  number = {5}
}

@article{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{\"u}rkner, Paul-Christian},
  year = {2019},
  url = {https://arxiv.org/abs/1903.08008?},
  journal = {arXiv preprint arXiv:1903.08008}
}

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

@article{voneshCompensatoryLarvalResponses2005,
  title = {Compensatory Larval Responses Shift Trade-Offs Associated with Predator-Induced Hatching Plasticity},
  author = {Vonesh, James R. and Bolker, Benjamin M.},
  year = {2005},
  volume = {86},
  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.},
  copyright = {\textcopyright{} 2005 by the Ecological Society of America},
  file = {/Users/solomonkurz/Zotero/storage/HTIQ7YL3/04-0535.html},
  journal = {Ecology},
  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)},
  language = {en},
  number = {6}
}

@article{watanabeAsymptoticEquivalenceBayes2010,
  title = {Asymptotic Equivalence of {{Bayes}} Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory},
  author = {Watanabe, Sumio},
  year = {2010},
  volume = {11},
  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},
  journal = {Journal of Machine Learning Research},
  number = {116}
}

@article{watsonPANASDevelopment1988,
  title = {Development and Validation of Brief Measures of Positive and Negative Affect: The {{PANAS}} Scales.},
  author = {Watson, David and Clark, Lee Anna and Tellegen, Auke},
  year = {1988},
  volume = {54},
  pages = {1063--1070},
  publisher = {{American Psychological Association}},
  doi = {10.1037/0022-3514.54.6.1063},
  url = {https://homepages.se.edu/cvonbergen/files/2013/01/Development-and-Validation-of-Brief-Measures-of-Positive-and-Negative-Affect_The-PANAS-Scales.pdf},
  journal = {Journal of personality and social psychology},
  number = {6}
}

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

@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{\"u}ller, 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},
  year = {2019},
  volume = {4},
  pages = {1686},
  doi = {10.21105/joss.01686},
  journal = {Journal of Open Source Software},
  number = {43}
}

@article{wilksLargesampleDistributionLikelihood1938,
  title = {The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses},
  author = {Wilks, S. S.},
  year = {1938},
  volume = {9},
  pages = {60--62},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0003-4851},
  doi = {10.1214/aoms/1177732360},
  url = {https://www.jstor.org/stable/2957648},
  urldate = {2020-06-01},
  journal = {The Annals of Mathematical Statistics},
  number = {1}
}

@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},
  year = {2019},
  month = mar,
  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{williamsBayesianNonlinearMixedeffects2019a,
  title = {A {{Bayesian}} Nonlinear Mixed-Effects Location Scale Model for Learning},
  author = {Williams, Donald R. and Zimprich, Daniel R. and Rast, Philippe},
  year = {2019},
  month = oct,
  volume = {51},
  pages = {1968--1986},
  issn = {1554-3528},
  doi = {10.3758/s13428-019-01255-9},
  url = {https://doi.org/10.3758/s13428-019-01255-9},
  urldate = {2020-08-05},
  abstract = {We present a Bayesian nonlinear mixed-effects location scale model (NL-MELSM). The NL-MELSM allows for fitting nonlinear functions to the location, or individual means, and the scale, or within-person variance. Specifically, in the context of learning, this model allows the within-person variance to follow a nonlinear trajectory, where it can be determined whether variability reduces during learning. It incorporates a sub-model that can predict nonlinear parameters for both the location and scale. This specification estimates random effects for all nonlinear location and scale parameters that are drawn from a common multivariate distribution. This allows estimation of covariances among the random effects, within and across the location and the scale. These covariances offer new insights into the interplay between individual mean structures and intra-individual variability in nonlinear parameters. We take a fully Bayesian approach, not only for ease of estimation but also for inference because it provides the necessary and consistent information for use in psychological applications, such as model selection and hypothesis testing. To illustrate the model, we use data from 333 individuals, consisting of three age groups, who participated in five learning trials that assessed verbal memory. In an exploratory context, we demonstrate that fitting a nonlinear function to the within-person variance, and allowing for individual variation therein, improves predictive accuracy compared to customary modeling techniques (e.g., assuming constant variance). We conclude by discussing the usefulness, limitations, and future directions of the NL-MELSM.},
  file = {/Users/solomonkurz/Zotero/storage/Z5PSDTI9/Williams et al. - 2019 - A Bayesian nonlinear mixed-effects location scale .pdf},
  journal = {Behavior Research Methods},
  language = {en},
  number = {5}
}

@article{williamsPuttingIndividualReliability2019,
  title = {Putting the Individual into Reliability: {{Bayesian}} Testing of Homogeneous within-Person Variance in Hierarchical Models},
  shorttitle = {Putting the {{Individual}} into {{Reliability}}},
  author = {Williams, Donald R. and Martin, Stephen R. and Rast, Philippe},
  year = {2019},
  month = jul,
  publisher = {{PsyArXiv}},
  doi = {10.31234/osf.io/hpq7w},
  url = {https://psyarxiv.com/hpq7w/},
  urldate = {2020-08-05},
  abstract = {Measurement reliability is a fundamental concept in psychology. It is traditionally considered a stable property of a questionnaire, measurement device, or experimental task. 
Although intraclass correlation coefficients (ICC) are often used to assess reliability in repeated measure designs, their descriptive nature depends upon the assumption of a common
within-person variance.
This work focuses on the presumption that each individual is adequately described by the average within-person variance in hierarchical models. And thus whether reliability generalizes to the individual level, which leads directly into the notion of individually varying ICCs. In particular, we introduce a novel approach, using the Bayes factor, wherein a researcher can directly test for homogeneous within-person variance in hierarchical models. Additionally, we introduce a membership model that allows for classifying which (and how many) individuals belong to the common variance model. The utility of our methodology is demonstrated on cognitive inhibition tasks. We find that heterogeneous within-person variance is a defining feature of these tasks, and in one case, the ratio between the largest to smallest within-person variance exceeded 20. This translates into a 10 fold difference in person-specific reliability! We also find that few individuals belong to the common variance model, and thus traditional reliability indices are potentially masking important individual variation. We discuss the implications of our findings and possible future directions. The methods are implemented in the R package vICC.},
  file = {/Users/solomonkurz/Zotero/storage/7X3329PI/Williams et al. - 2019 - Putting the Individual into Reliability Bayesian .pdf;/Users/solomonkurz/Zotero/storage/46GZSWKM/hpq7w.html}
}

@article{williamsSurfaceUnearthingWithinperson2019,
  title = {Beneath the Surface: {{Unearthing}} within-{{Person}} Variability and Mean Relations with {{Bayesian}} Mixed Models},
  shorttitle = {Beneath the {{Surface}}},
  author = {Williams, Donald R. and Rouder, Jeffrey and Rast, Philippe},
  year = {2019},
  month = jun,
  publisher = {{PsyArXiv}},
  doi = {10.31234/osf.io/gwatq},
  url = {https://osf.io/gwatq},
  urldate = {2020-08-05},
  abstract = {Mixed-effects models are becoming common in psychological science. Although they have many desirable features, there is still untapped potential that has not yet been fully realized. It is customary to view homogeneous variance as an assumption to satisfy. We argue to move beyond that perspective, and to view modeling within-person variance (``noise'') as an opportunity to gain a richer understanding of psychological processes. This can provide important insights into behavioral (in)stability. The technique to do so is based on the mixed-effects location scale model. The formulation can simultaneously estimate mixed-effects sub-models to both the mean (location) and within-person variance (scale) for clustered data common to psychology. We develop a framework that goes beyond assessing the sub-models in isolation of one another, and allows for testing structural relations between the mean and within-person variance with the Bayes factor. We first present a motivating example, which makes clear how the model can characterize mean\textendash variance relations. We then apply the method to reaction times gathered from two cognitive inference tasks. We find there are more individual differences in the within-person variance than the mean structure, as well as a complex web of structural mean\textendash variance relations in the random effects. This stands in contrast to the dominant view of within-person variance\textendash i.e., measurement ``error'' or ``noise.'' The results also point towards paradoxical within-person, as opposed to between-person, effects. That is, in both tasks, several people had \textbackslash emph\{slower\} and \textbackslash emph\{less\} variable incongruent responses. This contradicts the typical pattern, wherein \textbackslash emph\{larger\} means are expected to be \textbackslash emph\{more\} variable. We conclude with future directions. These span from methodological to theoretical inquires that can be answered with the presented methodology.}
}

@article{woodGeneralizedAdditiveModels,
  title = {Generalized {{Additive Models}}: An Introduction with {{R}}},
  author = {Wood, Simon N},
  pages = {397},
  file = {/Users/solomonkurz/Zotero/storage/W5HE99FH/Wood - Generalized Additive Models an introduction with .pdf},
  language = {en}
}

@book{woodGeneralizedAdditiveModels2017,
  title = {Generalized Additive Models: {{An}} Introduction with {{R}}},
  shorttitle = {Generalized {{Additive Models}}},
  author = {Wood, Simon N},
  year = {2017},
  month = may,
  edition = {Second Edition},
  publisher = {{CRC Press}},
  url = {https://www.routledge.com/Generalized-Additive-Models-An-Introduction-with-R-Second-Edition/Wood/p/book/9781498728331},
  abstract = {The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models.   The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book's R data package gamair, to enable use as a course text or for self-study. Simon N. Wood is a professor of Statistical Science at the University of Bristol, UK, and author of the R package mgcv.},
  isbn = {978-1-4987-2834-8},
  keywords = {Mathematics / Probability \& Statistics / General},
  language = {en}
}

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

@article{yarkoniChoosingPredictionExplanation2017,
  title = {Choosing Prediction over Explanation in Psychology: {{Lessons}} from Machine Learning},
  shorttitle = {Choosing Prediction over Explanation in Psychology},
  author = {Yarkoni, Tal and Westfall, Jacob},
  year = {2017},
  month = nov,
  volume = {12},
  pages = {1100--1122},
  issn = {1745-6916},
  doi = {10.1177/1745691617693393},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603289/},
  urldate = {2020-06-06},
  abstract = {Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism, but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.},
  file = {/Users/solomonkurz/Zotero/storage/79P37THN/Yarkoni and Westfall - 2017 - Choosing prediction over explanation in psychology.pdf},
  journal = {Perspectives on psychological science : a journal of the Association for Psychological Science},
  number = {6},
  pmcid = {PMC6603289},
  pmid = {28841086}
}

@book{yuDataIntegrationManipulation2020,
  title = {Data Integration, Manipulation and Visualization of Phylogenetic Trees},
  author = {Yu, Guangchuang},
  year = {2020},
  month = jul,
  url = {https://yulab-smu.github.io/treedata-book/},
  urldate = {2020-08-03},
  abstract = {Data Integration, Manipulation and Visualization of Phylogenetic Trees},
  file = {/Users/solomonkurz/Zotero/storage/BC6T6WAC/treedata-book.html}
}

@article{yuGgtreePackageVisualization2017,
  title = {{{ggtree}}: {{An}} r Package for Visualization and Annotation of Phylogenetic Trees with Their Covariates and Other Associated Data},
  shorttitle = {Ggtree},
  author = {Yu, Guangchuang and Smith, David K. and Zhu, Huachen and Guan, Yi and Lam, Tommy Tsan-Yuk},
  year = {2017},
  volume = {8},
  pages = {28--36},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.12628},
  url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12628},
  urldate = {2020-08-03},
  abstract = {We present an r package, ggtree, which provides programmable visualization and annotation of phylogenetic trees. ggtree can read more tree file formats than other softwares, including newick, nexus, NHX, phylip and jplace formats, and support visualization of phylo, multiphylo, phylo4, phylo4d, obkdata and phyloseq tree objects defined in other r packages. It can also extract the tree/branch/node-specific and other data from the analysis outputs of beast, epa, hyphy, paml, phylodog, pplacer, r8s, raxml and revbayes software, and allows using these data to annotate the tree. The package allows colouring and annotation of a tree by numerical/categorical node attributes, manipulating a tree by rotating, collapsing and zooming out clades, highlighting user selected clades or operational taxonomic units and exploration of a large tree by zooming into a selected portion. A two-dimensional tree can be drawn by scaling the tree width based on an attribute of the nodes. A tree can be annotated with an associated numerical matrix (as a heat map), multiple sequence alignment, subplots or silhouette images. The package ggtree is released under the artistic-2.0 license. The source code and documents are freely available through bioconductor (http://www.bioconductor.org/packages/ggtree).},
  copyright = {\textcopyright{} 2016 The Authors. Methods in Ecology and Evolution \textcopyright{} 2016 British Ecological Society},
  file = {/Users/solomonkurz/Zotero/storage/IR7IJ87T/Yu et al. - 2017 - ggtree an r package for visualization and annotat.pdf;/Users/solomonkurz/Zotero/storage/DB2B76PZ/2041-210X.html},
  journal = {Methods in Ecology and Evolution},
  keywords = {annotation,bioconductor,evolution,phylogeny,r package,visualization},
  language = {en},
  number = {1}
}

@article{yuTwoMethodsMapping2018,
  title = {Two Methods for Mapping and Visualizing Associated Data on Phylogeny Using Ggtree},
  author = {Yu, Guangchuang and Lam, Tommy Tsan-Yuk and Zhu, Huachen and Guan, Yi},
  year = {2018},
  month = dec,
  volume = {35},
  pages = {3041--3043},
  publisher = {{Oxford Academic}},
  issn = {0737-4038},
  doi = {10.1093/molbev/msy194},
  url = {https://academic.oup.com/mbe/article/35/12/3041/5142656},
  urldate = {2020-08-03},
  abstract = {Abstract.  Ggtree is a comprehensive R package for visualizing and annotating phylogenetic trees with associated data. It can also map and visualize associated},
  file = {/Users/solomonkurz/Zotero/storage/CG9PRJX6/Yu et al. - 2018 - Two Methods for Mapping and Visualizing Associated.pdf;/Users/solomonkurz/Zotero/storage/HBF6GL5J/5142656.html},
  journal = {Molecular Biology and Evolution},
  language = {en},
  number = {12}
}

@article{yuUsingGgtreeVisualize2020,
  title = {Using Ggtree to Visualize Data on Tree-like Structures},
  author = {Yu, Guangchuang},
  year = {2020},
  volume = {69},
  pages = {e96},
  issn = {1934-340X},
  doi = {10.1002/cpbi.96},
  url = {https://currentprotocols.onlinelibrary.wiley.com/doi/abs/10.1002/cpbi.96},
  urldate = {2020-08-03},
  abstract = {Ggtree is an R/Bioconductor package for visualizing tree-like structures and associated data. After 5 years of continual development, ggtree has been evolved as a package suite that contains treeio for tree data input and output, tidytree for tree data manipulation, and ggtree for tree data visualization. Ggtree was originally designed to work with phylogenetic trees, and has been expanded to support other tree-like structures, which extends the application of ggtree to present tree data in other disciplines. This article contains five basic protocols describing how to visualize trees using the grammar of graphics syntax, how to visualize hierarchical clustering results with associated data, how to estimate bootstrap values and visualize the values on the tree, how to estimate continuous and discrete ancestral traits and visualize ancestral states on the tree, and how to visualize a multiple sequence alignment with a phylogenetic tree. The ggtree package is freely available at https://www.bioconductor.org/packages/ggtree. \textcopyright{} 2020 by John Wiley \& Sons, Inc. Basic Protocol 1: Using grammar of graphics for visualizing trees Basic Protocol 2: Visualizing hierarchical clustering using ggtree Basic Protocol 3: Visualizing bootstrap values as symbolic points Basic Protocol 4: Visualizing ancestral status Basic Protocol 5: Visualizing a multiple sequence alignment with a phylogenetic tree},
  copyright = {\textcopyright{} 2020 John Wiley \& Sons, Inc.},
  file = {/Users/solomonkurz/Zotero/storage/WUMP5VUX/cpbi.html},
  journal = {Current Protocols in Bioinformatics},
  keywords = {grammar of graphics,phylogeny,tree associated data,tree structure,visualization},
  language = {en},
  number = {1}
}

@article{zhangCrossvalidationSelectingModel2015,
  title = {Cross-Validation for Selecting a Model Selection Procedure},
  author = {Zhang, Yongli and Yang, Yuhong},
  year = {2015},
  month = jul,
  volume = {187},
  pages = {95--112},
  issn = {0304-4076},
  doi = {10.1016/j.jeconom.2015.02.006},
  url = {http://www.sciencedirect.com/science/article/pii/S0304407615000305},
  urldate = {2020-06-03},
  abstract = {While there are various model selection methods, an unanswered but important question is how to select one of them for data at hand. The difficulty is due to that the targeted behaviors of the model selection procedures depend heavily on uncheckable or difficult-to-check assumptions on the data generating process. Fortunately, cross-validation (CV) provides a general tool to solve this problem. In this work, results are provided on how to apply CV to consistently choose the best method, yielding new insights and guidance for potentially vast amount of application. In addition, we address several seemingly widely spread misconceptions on CV.},
  file = {/Users/solomonkurz/Zotero/storage/W4YYPU24/S0304407615000305.html},
  journal = {Journal of Econometrics},
  keywords = {Adaptive procedure selection,Cross-validation,Cross-validation paradox,Data splitting ratio,Information criterion,LASSO,MCP,SCAD},
  language = {en},
  number = {1}
}

@book{@Boole_1854,
  author = {George Boole},
  title = {The Laws of Thought},
  year = {1854}
}

@incollection{Akaike_1998,
  title = {Information Theory and an Extension of the Maximum Likelihood Principle},
  booktitle = {Selected Papers of {{Hirotugu Akaike}}},
  author = {Akaike, Hirotogu},
  year = {1998},
  pages = {199--213},
  publisher = {{Springer}},
  url = {https://www.springer.com/gp/book/9780387983554}
}


@article{Student_1908,
	author = {Student, A.},
	title = {The  probable error of a  mean},
	year = 1908,
	journal = {Biometrika},
	volume = 6,
	pages = {1--2}
} 

@book{Fisher_1925,
  title = {A Manual for Statistical Researchers},
  author = {Ronald A. Fisher},
  year = {1925}
}

@book{McElreath_2020,
author = {McElreath, R.},
year = {2020},
month = {01},
pages = {1-469},
title = {Statistical rethinking: A bayesian course with examples in R and STAN, 2nd ed.},
}

@book{Feynman_1996,
  title={Lectures On Computation},
  author={Feynman, R.P. and Allen, R.},
  isbn={9780201489910},
  lccn={96025127},
  series={Advanced book program},
  url={https://books.google.com/books?id=-olQAAAAMAAJ},
  year={1996},
  publisher={Basic Books}
}

@article{Geman_1996,
	year = 1996,
	month = {oct},
	publisher = {Wiley},
	volume = {6},
	number = {4},
	pages = {365--378},
	author = {H{\'{e}}lyette Geman and Marc Yor},
	title = {{PRICING} {AND} {HEDGING} {DOUBLE}-{BARRIER} {OPTIONS}: A {PROBABILISTIC} {APPROACH}},
	journal = {Mathematical Finance}
}

@book{Kanwal_1989,
  author = {Ram P. Kanwal},
  title = {Generalized Functions: Theory and Technique, 2nd ed.},
  year = {1989},
  publisher = {Birkhauser}
}

@book{Citation_needed,
  title = {title needed}
}
@Book{Koenker_2005, 
  author = {Roger Koenker},
  year = {2005}, 
  title = {Quantile Regression (Econometric Society Monographs)}, 
  publisher = {Cambridge University Press},
  address = {Cambridge, U.K.},
}

@Book{Jaynes_2004,
  title = {Probability Theory: logic of science},
  author = {Edwin T. Jaynes, ed. G. Larry Bretthorst},
  year = {2004},
  publisher = {Cambridge University Press},
  address = {Cambridge, U.K.},
}


@Book{Polya_1954,
  title = {Mathemtics and Plausible Reasoning},
  author = {George Polya},
  year = {1954},
  publisher = {Princeton University Press},
  address = {Princeton, N.J.},
}

@Book{Smith_1955,
  title = {Precepts of Rhetoric},
  author = {Sydney J. Smith, S.J.},
  publisher = {The Holy Cross College Press},
  address = {Worcester, Ma.},
  year = {1955}
}

@incollection{Peirce_1883,
author = {Charles S. Peirce},
title = {A Theory of Probable Inference},
booktitle = {Studies in Logic by Members of the Johns Hopkins University},
editor = {Charles S. Peirce},
year = {1883},
address = {Boston, MA},
publisher = {Little, Brown, and Company},
pages = {126-181},
language = {English},
note = {From the Commens Bibliography | \url{http://www.commens.org/bibliography/collection_article/peirce-charles-s-1883-theory-probable-inference-studies-logic}},
}

@Book{Plato_Theaetetus,
  author = {Plato},
  title = {Theaetetus},
  year = {340 BCE},
  url = {http://www.perseus.tufts.edu/hopper/text?doc=Perseus%3Atext%3A1999.01.0172%3Atext%3DTheaet.%3Asection%3D210a}
}

@book{Aristotle_Organon,
  author = {Aristotle},
  title = {Organon and Other Works},
  translator = {W.D. Ross},
  url = {https://archive.org/details/AristotleOrganon},
  year = {50 BCE},
  publisher = {Andronicus of Rhodes}
}

@incollection{Lepore_2006,
	publisher = {North Holland},
	author = {Ernest Lepore and Matthew Stone},
	editor = {Dale Jacquette},
	booktitle = {Philosophy of Logic},
	pages = {173},
	year = {2006},
	title = {Logic and Semantic Analysis}
}

@Book{Potter_1994,
  author = {Potter, Vincent G.},
  title = {On Understanding Understanding -- 2nd ed.},
  place = {Bronx, N.Y.},
  publisher = {Fordham University Press}, 
  year = {1994},
  url = {https://www.fordhampress.com/9780823214860/on-understanding-understanding/},
  annotate = {Vincent Potter, S.J. taught me an advanced course at Fordham called Central Problems in Epistemology around 1973. The core reading for this course was Longergan's Insight.}
}

@book{Laplace_1902,
  title={A Philosophical Essay on Probabilities},
  author={de Laplace, P.S. and Truscott, F.W. and Emory, F.L.},
  series={A Philosophical Essay on Probabilities},
  url={https://books.google.com/books?id=WxoPAAAAIAAJ},
  year={1902},
  publisher={Wiley}
}

@article{Dolan_2017,
author = {Timothy E. Dolan},
title ={Framing Indeterminacy: Dialectical Analysis and Futures Studies},
journal = {World Futures Review},
volume = {10},
number = {1},
pages = {83-94},
year = {2018},
doi = {10.1177/1946756717739628},

URL = { 
        https://doi.org/10.1177/1946756717739628
    
},
eprint = { 
        https://doi.org/10.1177/1946756717739628
    
}
,
    abstract = { The dialectic is a very ancient device long used to comprehend historical process. Its versatility as a tool for informing on numerous issues of interest to those involved in futures studies is self-evident yet under-appreciated, not as a predicting device but as a framing one, much as with how catalysts work in chemistry. This piece first briefly describes the basics of the dialectical process and its application as an outcome framing method. Its connections with other forecasting methods especially with emerging issues analysis is discussed. The properties of dialectical processes are dynamic and do not necessarily conform to human-scale either temporally or spatially. Dialectical process related to social change and worldview are not subordinate to cyclic time except when artificially imposed as with political elections. The dialectical process being consistent with quantum mechanics especially as applied to technological impacts and legal enactment process will be discussed. In the latter instance, the dark matter and dark energy conundrum bear the hallmarks of a dialectical process thus far leading to an indeterminable resolution. While several examples of persistent issue contradictions to which dialectical analysis can be usefully applied are available, American health care policy is highlighted here. }
}

@Book{Smit_Trigeorgis_2004,
 title                = {Strategic Investment: Real Options and Games},
 author               = {Smit, Han and Trigeorgis, Lenos},
 publisher            = {Princeton University Press},
 year                 = {2004},
}

@Book{Heuer_1999,
  author = {Heuer, Richards J.},
  title = {Psychology Of Intelligence Analysis},
  place = {Washington, D.C.},
  publisher = {Center for the Study of Intelligence}, 
  year = {1999},
  url = {https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/PsychofIntelNew.pdf}
}

@book{Heuer_Pherson_2010,
 author = {Heuer,Jr., Richards J. and Pherson, Randolph H.},
 title = {Structured Analytic Techniques for Intelligence Analysis},
 year = {2010},
 publisher = {CQ Press},
} 

@book{gilovich_griffin_kahneman_2002, 
  author = {Gilovich, Griffin, Kahneman},
  place={Cambridge}, 
  title={Heuristics and Biases: The Psychology of Intuitive Judgment}, 
  DOI={10.1017/CBO9780511808098}, 
  publisher={Cambridge University Press}, 
  year={2002}
}

@article{Foote_2018,
  author = {William G. Foote},
  title = {Dialectical Intelligence Analysis: A New Optic},
  year = {2018}
}

@article{Fox_2010,
  author = {William P. Fox},
  title = {Game Theory and Optimization via Excel},
  journal = {Proceedings of the Twenty-second Annual International Conference on Technology in Collegiate Mathematics},
  url = {http://archives.math.utk.edu/ICTCM/i/22/S070.html},
  volume = {22},
  year = {2010}
}

@article{Harsanyi_1967,
  author = {John C. Harsanyi},
  title = {Games With Incomplete Information Played by "Bayesian" Players, I},
  journal = {Management Science},
  number = {3},
  pages = {159-182},
  year = {1967}
}

@article{Slantchev_2014,
  author = {Branislav L. Slantchev},
  title = {National Security Strategy: Games and Information},
  journal = {Department of Political Science, University of California at San Diego},
  url = {http://slantchev.ucsd.edu/courses/ps142j/lectures/games-and-information.pdf},
  year = {2014}
}

@article{Buffa_2018,
  author = {Cristina Buffa}, 
  title = {The Magnificat and the Song of Hannah: Comparing Social Conditions},
  journal = {Revue des sciences religieuses}, 
  volume = {92},
  number = {3},
  year = {2018},
  pages = {377-392},
}

@article{Bengochea_2013,
  author = {Bengochea, Gabriel and G, Gabriel},
  year = {2013},
  month = {05},
  pages = {},
  title = {Mikusi\'nski's Operational Calculus with Algebraic Foundations and Applications to Bessel Functions},
  volume = {25},
  journal = {Integral Transforms and Special Functions},
  doi = {10.1080/10652469.2013.838956}
}

@article{Dimovski_1966,
author = {Dimovski, Ivan},
year = {1966},
month = {01},
pages = {1111-1114},
title = {Operational calculus for a class of differential operators},
volume = {19},
journal = {C.R. Acad. Bulg. Sci.}
}

@article{Bengachea_2018,
  author = {Bengochea, Gabriel and Verde-Star, Luis and Ortigueira, Manuel},
  year = {2018},
  month = {01},
  pages = {},
  title = {Operational method for the solution of ordinary differential equations using Hermite series},
  volume = {23},
  journal = {Mathematical Communications}
}

@book{Dorfman_Samuelson_Solow_1958,
  author = {Robert Dorfman, Paul A. Samuelson, Robert M. Solow},
  title = {Linear Programming and Economic Analysis},
  publisher = {McGraw-Hill},
  year = {1958}
}

@book{Yosida_1983,
  author = {K{\^{o}}saku Yosida},
  title = {Operational Calculus: A Theory of Hyperfunctions},
  publisher = {Springer-Verlag},
  year = {1983}
}

@book{Mikusinski_1958,
  author = {Jan Mikusi{\'{n}}ski},
  title = {Operational Calculus},
  publisher = {Pergamon Press},
  year = {1958}
}

@book{Varian_1992,
  title = {Microeconomic Analysis},
  author = {Varian, H.R.},
  year = {1992},
  publisher = {Norton}
}

@article{Bandyopadhyay_Subhay_Sandler_2014,
    author = {Bandyopadhyay, Subhayu and Sandler, Todd and Younas, Javed},
    title = "{Foreign direct investment, aid, and terrorism}",
    journal = {Oxford Economic Papers},
    volume = {66},
    number = {1},
    pages = {25-50},
    year = {2013},
    month = {07},
    abstract = "{This paper constructs a theoretical model to investigate the relationship between the two major forms of terrorism and foreign direct investment (FDI). We analyze with various estimators how these relationships are affected by foreign aid flows by focusing on 78 developing countries for 1984-2008. Both types of terrorism are found to depress FDI. Aggregate aid mitigates the negative consequences of domestic and transnational terrorism, but this aid appears more robust in ameliorating the adverse effect of domestic terrorism. However, when aid is subdivided, bilateral aid is effective in reducing the adverse effects of transnational terrorism on FDI, whereas multilateral aid is effective in curbing the adverse effects of domestic terrorism on FDI. For transnational terrorism, there is evidence in the literature that donor countries earmark some bilateral aid to counterterrorism. Aid ability to curb the risk to FDI from terrorism is important because FDI is an important engine of development.}",
    issn = {0030-7653},
    doi = {10.1093/oep/gpt026},
    url = {https://dx.doi.org/10.1093/oep/gpt026},
    eprint = {http://oup.prod.sis.lan/oep/article-pdf/66/1/25/17481359/gpt026.pdf},
}

@article{Sandler_2014,
    author = {Sandler, Todd},
    title = "{Terrorism and counterterrorism: an overview}",
    journal = {Oxford Economic Papers},
    volume = {67},
    number = {1},
    pages = {1-20},
    year = {2014},
    month = {11},
    abstract = "{This introduction sets the stage for the articles collected in this special issue of Oxford Economic Papers. It begins by introducing essential concepts including domestic terrorism, transnational terrorism, defensive actions, proactive countermeasures, and guerrilla warfare. Three terrorist event databases, used by seven of the articles, are briefly introduced. These data sets are then used to display some stylized facts about domestic and transnational terrorism during the past four decades. Next, some essential strategic distinctions are drawn between defensive and proactive measures in the case of transnational terrorism when multiple countries are confronted by a common terrorist group. These strategic concerns vanish for domestic terrorism as a central government is able to internalize potential externalities. Finally, the key findings of the articles in the special issue are highlighted in two tables.}",
    eprint = {http://oup.prod.sis.lan/oep/article-pdf/67/1/1/9540903/gpu039.pdf},
}

@article{Masud_1981,
 ISSN = {01605682, 14769360},
 URL = {http://www.jstor.org/stable/2581556},
 abstract = {This paper introduces a new solution method based on Goal Programming for Multiple Objective Decision Making (MODM) problems. The method, called Interactive Sequential Goal Programming (ISGP), combines and extends the attractive features of both Goal Programming and interactive solution approaches for MODM problems. ISGP is applicable to both linear and non-linear problems. It uses existing single objective optimization techniques and, hence, available computer codes utilizing these techniques can be adapted for use in ISGP. The non-dominance of the "best-compromise" solution is assured. The information required from the decision maker in each iteration is simple. The proposed method is illustrated by solving a nutrition problem.},
 author = {Abu S. Masud and C. L. Hwang},
 journal = {The Journal of the Operational Research Society},
 number = {5},
 pages = {391-400},
 publisher = {Palgrave Macmillan Journals},
 title = {Interactive Sequential Goal Programming},
 volume = {32},
 year = {1981}
}

@book{Lonergan_1970,
  author = {Bernard J.F. Lonergan},
  title = {Insight: A Study of Human Understanding},
  publisher = {Philosophical Library},
  year = {1970}
}

@incollection{Bruce_2014,
  author      = {Bruce, James B.},
  title       = {Making Analysis More Reliable: Why Epistemology Matters to Intelligence},
  editor      = "Jan Fagerberg and David C. Mowery and Richard R. Nelson",
  booktitle   = {Analyzing Intelligence: National Security Practitioners Perspectives},
  publisher   = {Georgetown University Press},
  year        = {2014}
}

@article{Collier_2005,
  author = {Michael W. Collier},
  title = {A Pragmatic Approach to Developing Intelligence Analysis}, 
  journal = {Defense Intelligence Journal},
  volume = {14},
  number = {3},
  pages = {17-35},
  year = {2005}
}