References

Aden-Buie, G. (2022). ggpomological: Pomological plot theme for ggplot2 [Manual]. https://github.com/gadenbuie/ggpomological
Agresti, A. (2015). Foundations of linear and generalized linear models. John Wiley & Sons. https://www.wiley.com/en-us/Foundations+of+Linear+and+Generalized+Linear+Models-p-9781118730034
Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In Selected papers of Hirotugu Akaike (pp. 199–213). Springer. https://www.springer.com/gp/book/9780387983554
Allen, M. A., Flynn, M. E., Machain, C. M., & Stravers, A. (2020). Outside the wire: US military deployments and public opinion in host states. American Political Science Review, 114(2), 326–341. https://doi.org/10.1017/S0003055419000868
Amlie-Lefond, C., Shaw, D. W., Cooper, A., Wainwright, M. S., Kirton, A., Felling, R. J., Abraham, M. G., Mackay, M. T., Dowling, M. M., Torres, M., et al. (2020). Risk of intracranial hemorrhage following intravenous tPA (Tissue-Type Plasminogen Activator) for acute stroke is low in children. Stroke, 51(2), 542–548. https://doi.org/10.1161/STROKEAHA.119.027225
Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567(7748, 7748), 305–307. https://doi.org/10.1038/d41586-019-00857-9
Angrist, J. D., & Keueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? The Quarterly Journal of Economics, 106(4), 979–1014. https://doi.org/10.2307/2937954
Aono, Y. (2012). Long-term change in climate and floral phenophase. Chikyu Kankyo (Global Environment), 17. http://atmenv.envi.osakafu-u.ac.jp/aono/kyophenotemp4/
Aono, Y., & Kazui, K. (2008). Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century. International Journal of Climatology, 28(7), 905–914. https://doi.org/10.1002/joc.1594
Aono, Y., & Saito, S. (2010). Clarifying springtime temperature reconstructions of the medieval period by gap-filling the cherry blossom phenological data series at Kyoto, Japan. International Journal of Biometeorology, 54(2), 211–219. https://doi.org/10.1007/s00484-009-0272-x
Arnold, J. B. (2021). ggthemes: Extra themes, scales and geoms for ’ggplot2’. https://CRAN.R-project.org/package=ggthemes
Atkins, D. C., Baldwin, S. A., Zheng, C., Gallop, R. J., & Neighbors, C. (2013). A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors : Journal of the Society of Psychologists in Addictive Behaviors, 27(1), 166–177. https://doi.org/10.1037/a0029508
Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48(1), 5–37. https://doi.org/10.1016/j.jsp.2009.10.001
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278. https://doi.org/10.1016/j.jml.2012.11.001
Barrett, M. (2022a). An introduction to ggdag. https://CRAN.R-project.org/package=ggdag/vignettes/intro-to-ggdag.html
Barrett, M. (2022b). ggdag: Analyze and create elegant directed acyclic graphs. https://CRAN.R-project.org/package=ggdag
Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2021). Modern data science with R (2nd edition). Taylor & Francis Group, LLC. https://mdsr-book.github.io/mdsr2e/
Beheim, B., Atkinson, Q. D., Bulbulia, J., Gervais, W., Gray, R. D., Henrich, J., Lang, M., Monroe, M. W., Muthukrishna, M., Norenzayan, A., et al. (2021). Treatment of missing data determined conclusions regarding moralizing gods. Nature, 595(7866), E29–E34. https://doi.org/10.1038/s41586-019-1043-4
Betancourt, M. (2018). Bayes sparse regression. https://betanalpha.github.io/assets/case_studies/bayes_sparse_regression.html
Betancourt, M. (2017). Robust Gaussian processes in Stan. https://betanalpha.github.io/assets/case_studies/gp_part3/part3.html
Bickel, P. J., Hammel, E. A., & O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187(4175), 398–404. https://doi.org/10.1126/science.187.4175.398
Boesch, C., Bombjaková, D., Meier, A., & Mundry, R. (2019). Learning curves and teaching when acquiring nut-cracking in humans and chimpanzees. Scientific Reports, 9(1, 1), 1515. https://doi.org/10.1038/s41598-018-38392-8
Borges, JL. (1941). El jardin de senderos que se bifurcan. Buenos Aires: Sur. Translated by D. A. Yates (1964). In Labyrinths: Selected Stories & Other Writings (pp. 19–29). New Directions.
Brilleman, S., Crowther, M., Moreno-Betancur, M., Buros Novik, J., & Wolfe, R. (2018). Joint longitudinal and time-to-event models via Stan. https://github.com/stan-dev/stancon_talks/
Bryan, J., the STAT 545 TAs, & Hester, J. (2020). Happy Git and GitHub for the useR. https://happygitwithr.com
Bürkner, P.-C. (2022a). Estimating distributional models with brms. https://CRAN.R-project.org/package=brms/vignettes/brms_distreg.html
Bürkner, P.-C. (2022b). Define custom response distributions with brms. https://CRAN.R-project.org/package=brms/vignettes/brms_customfamilies.html
Bürkner, P.-C. (2022c). Estimating monotonic effects with brms. https://CRAN.R-project.org/package=brms/vignettes/brms_monotonic.html
Bürkner, P.-C. (2022d). Estimating multivariate models with brms. https://CRAN.R-project.org/package=brms/vignettes/brms_multivariate.html
Bürkner, P.-C. (2022e). Estimating non-linear models with brms. https://CRAN.R-project.org/package=brms/vignettes/brms_nonlinear.html
Bürkner, P.-C. (2022f). Estimating phylogenetic multilevel models with brms. https://CRAN.R-project.org/package=brms/vignettes/brms_phylogenetics.html
Bürkner, P.-C. (2022g). Handle missing values with brms. https://CRAN.R-project.org/package=brms/vignettes/brms_missings.html
Bürkner, P.-C. (2022h). Parameterization of response distributions in brms. https://CRAN.R-project.org/package=brms/vignettes/brms_families.html
Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28. https://doi.org/10.18637/jss.v080.i01
Bürkner, P.-C. (2018). Advanced Bayesian multilevel modeling with the R package brms. The R Journal, 10(1), 395–411. https://doi.org/10.32614/RJ-2018-017
Bürkner, P.-C. (2022i). brms reference manual, Version 2.18.0. https://CRAN.R-project.org/package=brms/brms.pdf
Bürkner, P.-C. (2022j). brms: Bayesian regression models using ’Stan. https://CRAN.R-project.org/package=brms
Bürkner, P.-C., & Charpentier, E. (2020). Modelling monotonic effects of ordinal predictors in Bayesian regression models. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12195
Bürkner, P.-C., Gabry, J., Kay, M., & Vehtari, A. (2022). posterior: Tools for working with posterior distributions. https://CRAN.R-project.org/package=posterior
Bürkner, P.-C., & Vuorre, M. (2019). Ordinal regression models in psychology: A tutorial. Advances in Methods and Practices in Psychological Science, 2(1), 77–101. https://doi.org/10.1177/2515245918823199
Carvalho, C. M., Polson, N. G., & Scott, J. G. (2009). Handling sparsity via the horseshoe. Artificial Intelligence and Statistics, 73–80. http://proceedings.mlr.press/v5/carvalho09a/carvalho09a.pdf
Casella, G., & George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician, 46(3), 167–174. https://doi.org/10.1080/00031305.1992.10475878
Casillas, J. V. (2021). Interlingual interactions elicit performance mismatches not “compromise” categories in early bilinguals: Evidence from meta-analysis and coronal stops. Languages, 6(1), 9. https://doi.org/10.3390/languages6010009
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences (Third Edition). Routledge. https://doi.org/10.4324/9780203774441
Cover, T. M., & Thomas, J. A. (2006). Elements of information theory (2nd Edition). John Wiley & Sons. https://www.wiley.com/en-us/Elements+of+Information+Theory%2C+2nd+Edition-p-9780471241959
Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29. https://doi.org/10.1177/0956797613504966
Cushman, F., Young, L., & Hauser, M. (2006). The role of conscious reasoning and intuition in moral judgment: Testing three principles of harm. Psychological Science, 17(12), 1082–1089. https://doi.org/10.1111/j.1467-9280.2006.01834.x
Davis, F. P., Nern, A., Picard, S., Reiser, M. B., Rubin, G. M., Eddy, S. R., & Henry, G. L. (2020). A genetic, genomic, and computational resource for exploring neural circuit function. Elife, 9, e50901. https://doi.org/10.7554/eLife.50901
de Rooij, M., & Weeda, W. (2020). Cross-validation: A method every psychologist should know. Advances in Methods and Practices in Psychological Science, 3(2), 248–263. https://doi.org/10.1177/2515245919898466
Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. Springer. https://link.springer.com/book/10.1007/978-1-4419-0118-7
Efron, B., & Morris, C. (1977). Stein’s paradox in statistics. Scientific American, 236(5), 119–127. https://doi.org/10.1038/scientificamerican0577-119
Enders, C. K. (2022). Applied missing data analysis (Second Edition). Guilford Press. http://www.appliedmissingdata.com/
Fernández i Marín, X. (2016). ggmcmc: Analysis of MCMC samples and Bayesian inference. Journal of Statistical Software, 70(9), 1–20. https://doi.org/10.18637/jss.v070.i09
Fernández i Marín, X. (2021). ggmcmc: Tools for analyzing MCMC simulations from Bayesian inference [Manual]. https://CRAN.R-project.org/package=ggmcmc
Freckleton, R. P. (2002). On the misuse of residuals in ecology: Regression of residuals vs. Multiple regression. Journal of Animal Ecology, 71(3), 542–545. https://doi.org/10.1046/j.1365-2656.2002.00618.x
Gabry, J. (2022). Plotting MCMC draws using the bayesplot package. https://CRAN.R-project.org/package=bayesplot/vignettes/plotting-mcmc-draws.html
Gabry, J., & Goodrich, B. (2022). rstanarm: Bayesian applied regression modeling via stan [Manual]. https://CRAN.R-project.org/package=rstanarm
Gabry, J., & Mahr, T. (2022). bayesplot: Plotting for Bayesian models. https://CRAN.R-project.org/package=bayesplot
Gabry, J., & Modrák, M. (2022). Visual MCMC diagnostics using the bayesplot package. https://CRAN.R-project.org/package=bayesplot/vignettes/visual-mcmc-diagnostics.html
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389–402. https://doi.org/10.1111/rssa.12378
Garnier, S. (2021). viridis: Default color maps from ’matplotlib’ [Manual]. https://CRAN.R-project.org/package=viridis
Gelman, A. (2005). Analysis of variance–Why it is more important than ever. Annals of Statistics, 33(1), 1–53. https://doi.org/10.1214/009053604000001048
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1(3), 515–534. https://doi.org/10.1214/06-BA117A
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (Third Edition). CRC press. https://stat.columbia.edu/~gelman/book/
Gelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2019). R-squared for Bayesian regression models. The American Statistician, 73(3), 307–309. https://doi.org/10.1080/00031305.2018.1549100
Gelman, A., & Greenland, S. (2019). Are confidence intervals better termed “uncertainty intervals”? BMJ, l5381. https://doi.org/10.1136/bmj.l5381
Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press. https://doi.org/10.1017/9781139161879
Gelman, A., & Imbens, G. (2019). Why high-order polynomials should not be used in regression discontinuity designs. Journal of Business & Economic Statistics, 37(3), 447–456. https://doi.org/10.1080/07350015.2017.1366909
Gelman, A., & Little, T. C. (1997). Postratification into many categories using hierarchical logistic regression. Survey Methodology, 23, 127–135. https://stat.columbia.edu/~gelman/research/published/poststrat3.pdf
Gelman, A., & Loken, E. (2013). 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. 17. https://stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf
Gelman, A., Simpson, D., & Betancourt, M. (2017). The prior can often only be understood in the context of the likelihood. Entropy, 19(10, 10), 555. https://doi.org/10.3390/e19100555
Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician, 60(4), 328–331. https://doi.org/10.1198/000313006X152649
Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(6), 721–741. https://doi.org/10.1109/TPAMI.1984.4767596
Girard, J. M., Cohn, J. F., Yin, L., & Morency, L.-P. (2021). Reconsidering the duchenne smile: Formalizing and testing hypotheses about eye constriction and positive emotion. Affective Science, 1–16. https://doi.org/10.1007/s42761-020-00030-w
Gohel, D. (2023). Using the flextable R package. https://ardata-fr.github.io/flextable-book/
Gohel, D. (2022). flextable: Functions for tabular reporting [Manual]. https://CRAN.R-project.org/package=flextable
Grafen, A., & Hails, R. (2002). Modern statistics for the life sciences. Oxford University Press. https://global.oup.com/academic/product/modern-statistics-for-the-life-sciences-9780199252312?
Grantham, N. (2019). ggdark: Dark mode for ’ggplot2’ themes [Manual]. https://CRAN.R-project.org/package=ggdark
Grolemund, G., & Wickham, H. (2017). R for data science. O’Reilly. https://r4ds.had.co.nz
Haines, N., Vassileva, J., & Ahn, W.-Y. (2018). The outcome-representation learning model: A novel reinforcement learning model of the Iowa Gambling Task. Cognitive Science, 42(8), 2534–2561. https://doi.org/10.1111/cogs.12688
Hamaker, E. L., & Dolan, C. V. (2009). Idiographic data analysis: Quantitative methodsfrom simple to advanced. In J. Valsiner, P. C. M. Molenaar, M. C. D. P. Lyra, & N. Chaudhary (Eds.), Dynamic process methodology in the social and developmental sciences (pp. 191–216). Springer. https://doi.org/10.1007/978-0-387-95922-1_9
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media. https://doi.org/10.1007/978-0-387-84858-7
Hauer, E. (2004). The harm done by tests of significance. Accident Analysis & Prevention, 36(3), 495–500. https://doi.org/10.1016/S0001-4575(03)00036-8
Hauser, M., Cushman, F., Young, L., Jin, R. K.-X., & Mikhail, J. (2007). A dissociation between moral judgments and justifications. Mind & Language, 22(1), 1–21. https://doi.org/10.1111/j.1468-0017.2006.00297.x
Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications. https://www.guilford.com/books/Introduction-to-Mediation-Moderation-and-Conditional-Process-Analysis/Andrew-Hayes/9781462534654
Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press. https://socviz.co/
Hedeker, D., Mermelstein, R. J., & Demirtas, H. (2008). An application of a mixed-effects location scale model for analysis of ecological momentary assessment (EMA) data. Biometrics, 64(2), 627–634. https://doi.org/10.1111/j.1541-0420.2007.00924.x
Hedeker, D., Mermelstein, R. J., & Demirtas, H. (2012). Modeling between- and within-subject variance in ecological momentary assessment (EMA) data using mixed-effects location scale models. Statistics in Medicine, 31(27). https://doi.org/10.1002/sim.5338
Henderson, E. (2022). ghibli: Studio ghibli colour palettes [Manual]. https://CRAN.R-project.org/package=ghibli
Henry, L., & Wickham, H. (2020). purrr: Functional programming tools. https://CRAN.R-project.org/package=purrr
Hewitt, C. G. (1921). The conservation of the wild life of Canada. Charles Scribner’s Sons.
Hilbe, J. M. (2011). Negative binomial regression (Second Edition). https://doi.org/10.1017/CBO9780511973420
Hinde, K., & Milligan, L. A. (2011). Primate milk: Proximate mechanisms and ultimate perspectives. Evolutionary Anthropology: Issues, News, and Reviews, 20(1), 9–23. https://doi.org/10.1002/evan.20289
Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change (1 edition). Routledge. https://www.routledge.com/Longitudinal-Analysis-Modeling-Within-Person-Fluctuation-and-Change/Hoffman/p/book/9780415876025
Howell, N. (2001). Demography of the dobe! Kung (2nd Edition). Routledge. https://www.routledge.com/Demography-of-the-Dobe-Kung/Howell/p/book/9780202306490
Howell, N. (2010). Life histories of the Dobe! Kung: Food, fatness, and well-being over the life span (Vol. 4). Univ of California Press. https://www.ucpress.edu/book/9780520262348/life-histories-of-the-dobe-kung
Johnson, W., Carothers, A., & Deary, I. J. (2008). Sex differences in variability in general intelligence: A new look at the old question. Perspectives on Psychological Science, 3(6), 518–531. https://doi.org/10.1111/j.1745-6924.2008.00096.x
Kahle, D., & Stamey, J. (2017). invgamma: The inverse gamma distribution [Manual]. https://CRAN.R-project.org/package=invgamma
Kale, A., Kay, M., & Hullman, J. (2020). Visual reasoning strategies for effect size judgments and decisions. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2020.3030335
Kay, M. (2021). Extracting and visualizing tidy draws from brms models. https://mjskay.github.io/tidybayes/articles/tidy-brms.html
Kay, M. (2020). Marginal distribution of a single correlation from an LKJ distribution. https://mjskay.github.io/ggdist/reference/lkjcorr_marginal.html
Kay, M. (2022). tidybayes: Tidy data and ’geoms’ for Bayesian models. https://CRAN.R-project.org/package=tidybayes
Kennedy, L., & Gelman, A. (2021). Know your population and know your model: Using model-based regression and poststratification to generalize findings beyond the observed sample. Psychological Methods, 26(5), 547–558. https://doi.org/10.1037/met0000362
Kievit, R., Frankenhuis, W. E., Waldorp, L., & Borsboom, D. (2013). Simpson’s paradox in psychological science: A practical guide. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00513
Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Alper, S., Aveyard, M., Axt, J. R., Babalola, M. T., Bahník, Š., Batra, R., Berkics, M., Bernstein, M. J., Berry, D. R., Bialobrzeska, O., Binan, E. D., Bocian, K., Brandt, M. J., Busching, R., … Nosek, B. A. (2018). Many Labs 2: Investigating variation in replicability across samples and settings. Advances in Methods and Practices in Psychological Science, 1(4), 443–490. https://doi.org/10.1177/2515245918810225
Kline, M. A., & Boyd, R. (2010). Population size predicts technological complexity in Oceania. Proceedings of the Royal Society B: Biological Sciences, 277(1693), 2559–2564. https://doi.org/10.1098/rspb.2010.0452
Kolczynska, M., Bürkner, P.-C., Kennedy, L., & Vehtari, A. (2020). Trust in state institutions in Europe, 1989-2019. SocArXiv. https://doi.org/10.31235/osf.io/3v5g7
Koster, J. M., & Leckie, G. (2014). Food sharing networks in lowland Nicaragua: An application of the social relations model to count data. Social Networks, 38, 100–110. https://doi.org/10.1016/j.socnet.2014.02.002
Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press. https://sites.google.com/site/doingbayesiandataanalysis/
Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79–86. https://doi.org/10.1214/aoms/1177729694
Kurz, A. S. (2023a). Doing Bayesian data analysis in brms and the tidyverse (Version 1.1.0). https://bookdown.org/content/3686/
Kurz, A. S. (2023b). Statistical Rethinking with brms, ggplot2, and the tidyverse (version 1.3.0). https://bookdown.org/content/3890/
Kurz, A. S. (2023c). Recoding Introduction to mediation, moderation, and conditional process analysis (version 1.3.0). https://bookdown.org/content/b472c7b3-ede5-40f0-9677-75c3704c7e5c/
Kurz, A. S. (2021). Applied Longitudinal Data Analysis in brms and the tidyverse (version 0.0.2). https://bookdown.org/content/4253/
Kurz, A. S., DeBeer, B. B., Kimbrel, N. A., Morissette, S. B., & Meyer, E. C. (2019, October 16). Even with treatment, functional impairment and quality of life remain remarkably stable over two years in post-9/11 Iraq and Afghanistan war veterans. The 4th Annual San Antonio Combat PTSD Conference. https://osf.io/vekpf/
Linnebo, Ø. (2018). Platonism in the philosophy of mathematics. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Spring 2018). Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/spr2018/entries/platonism-mathematics/
Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis with missing data. John Wiley & Sons. https://www.wiley.com/en-us/Statistical+Analysis+with+Missing+Data%2C+3rd+Edition-p-9780470526798
Lotka, A. J. (1925). Principles of physical biology. Waverly.
Matejka, J., & Fitzmaurice, G. (2017). Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing. https://www.autodesk.com/research/publications/same-stats-different-graphs
McElreath, R. (2020a). Statistical rethinking: A Bayesian course with examples in R and Stan (Second Edition). CRC Press. https://xcelab.net/rm/statistical-rethinking/
McElreath, R. (2015). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press. https://xcelab.net/rm/statistical-rethinking/
McElreath, R. (2020b). rethinking R package. https://xcelab.net/rm/software/
Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66(1), 195–244. https://doi.org/10.2466/pr0.1990.66.1.195
Merkle, E. C., Fitzsimmons, E., Uanhoro, J., & Goodrich, B. (2021). Efficient Bayesian structural equation modeling in Stan. Journal of Statistical Software, 100(6), 1–22. https://doi.org/10.18637/jss.v100.i06
Merkle, E. C., & Rosseel, Y. (2018). blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software, 85(4), 1–30. https://doi.org/10.18637/jss.v085.i04
Merkle, E. C., Rosseel, Y., & Goodrich, B. (2022). blavaan: Bayesian latent variable analysis. https://CRAN.R-project.org/package=blavaan
Müller, K., & Wickham, H. (2022). tibble: Simple data frames. https://CRAN.R-project.org/package=tibble
Navarro, D. (2019). Learning statistics with R. https://learningstatisticswithr.com
Navarro, D. J. (2019). Between the devil and the deep blue sea: Tensions between scientific judgement and statistical model selection. Computational Brain & Behavior, 2(1), 28–34. https://doi.org/10.1007/s42113-018-0019-z
Nogueira, R. G., Jadhav, A. P., Haussen, D. C., Bonafe, A., Budzik, R. F., Bhuva, P., Yavagal, D. R., Ribo, M., Cognard, C., Hanel, R. A., et al. (2018). Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. New England Journal of Medicine, 378(1), 11–21. https://doi.org/10.1056/NEJMoa1706442
Nowosad, J. (2019). rcartocolor: ’CARTOColors’ palettes. https://CRAN.R-project.org/package=rcartocolor
Nunn, N., & Puga, D. (2012). Ruggedness: The blessing of bad geography in Africa. Review of Economics and Statistics, 94(1), 20–36. https://doi.org/10.1162/REST_a_00161
Paananen, T., Bürkner, P.-C., Vehtari, A., & Gabry, J. (2020). Avoiding model refits in leave-one-out cross-validation with moment matching. https://CRAN.R-project.org/package=loo/vignettes/loo2-moment-matching.html
Paananen, T., Piironen, J., Bürkner, P.-C., & Vehtari, A. (2020). Implicitly adaptive importance sampling. http://arxiv.org/abs/1906.08850
Paradis, Emmanuel, Blomberg, S., Bolker, B., Brown, J., Claramunt, S., Claude, J., Cuong, H. S., Desper, R., Didier, G., Durand, B., Dutheil, J., Ewing, R., Gascuel, O., Guillerme, T., Heibl, C., Ives, A., Jones, B., Krah, F., Lawson, D., … de Vienne, D. (2022). ape: Analyses of phylogenetics and evolution [Manual]. https://CRAN.R-project.org/package=ape
Paradis, E., & Schliep, K. (2019). ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 35, 526–528. https://doi.org/10.1093/bioinformatics/bty633
Park, D. K., Gelman, A., & Bafumi, J. (2004). Bayesian multilevel estimation with poststratification: State-level estimates from national polls. Political Analysis, 12(4), 375–385. https://www.jstor.org/stable/25791784
Pedersen, T. L. (2022). patchwork: The composer of plots. https://CRAN.R-project.org/package=patchwork
Peng, R. D. (2022). R programming for data science. https://bookdown.org/rdpeng/rprogdatascience/
Peng, R. D., Kross, S., & Anderson, B. (2017). Mastering software development in {}R{}. https://github.com/rdpeng/RProgDA
Pivot data from wide to long pivot_longer. (2020). https://tidyr.tidyverse.org/reference/pivot_longer.html
Pivoting. (2020). https://tidyr.tidyverse.org/articles/pivot.html
Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Working Papers, 8. http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Drafts/Plummer.pdf
R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
R Library Contrast Coding Systems for categorical variables. (n.d.). UCLA: Statistical Consulting Group. Retrieved October 14, 2020, from https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
Ram, K., & Wickham, H. (2018). wesanderson: A Wes Anderson palette generator [Manual]. https://CRAN.R-project.org/package=wesanderson
Rast, P., Hofer, S. M., & Sparks, C. (2012). Modeling individual differences in within-person variation of negative and positive affect in a mixed effects location scale model using BUGS/JAGS. Multivariate Behavioral Research, 47(2), 177–200. https://doi.org/10.1080/00273171.2012.658328
Revelle, W. (2022). psych: Procedures for psychological, psychometric, and personality research. https://CRAN.R-project.org/package=psych
Ripley, B. (2022). MASS: Support functions and datasets for venables and Ripley’s MASS. https://CRAN.R-project.org/package=MASS
Roback, P., & Legler, J. (2021). Beyond multiple linear regression: Applied generalized linear models and multilevel models in R. CRC Press. https://bookdown.org/roback/bookdown-BeyondMLR/
Robert, C., & Casella, G. (2011). A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data. Statistical Science, 26(1), 102–115. https://arxiv.org/pdf/0808.2902.pdf
Robinson, D., Hayes, A., & Couch, S. (2022). broom: Convert statistical objects into tidy tibbles [Manual]. https://CRAN.R-project.org/package=broom
Ross, C. T., Winterhalder, B., & McElreath, R. (2020). Racial disparities in police use of deadly force against unarmed individuals persist after appropriately benchmarking shooting data on violent crime rates. Social Psychological and Personality Science, 1948550620916071. https://doi.org/10.1177/1948550620916071
Rubin, Donald B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91(434), 473–489. https://doi.org/10.1080/01621459.1996.10476908
Rubin, Donald B. (1976). Inference and missing data. Biometrika, 63(3), 581–592. https://doi.org/10.1093/biomet/63.3.581
Rubin, Donald B. (1987). Multiple imputation for nonresponse in surveys. John Wiley & Sons Inc. https://doi.org/10.1002/9780470316696
Rudis, B. (2020). statebins: Create united states uniform cartogram heatmaps [Manual]. https://CRAN.R-project.org/package=statebins
Rudis, B., Ross, N., & Garnier, S. (2018). The viridis color palettes. https://cran.r-project.org/package=viridis/vignettes/intro-to-viridis.html
Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series b (Statistical Methodology), 71(2), 319–392. https://doi.org/10.1111/j.1467-9868.2008.00700.x
Schloerke, B., Crowley, J., Di Cook, Briatte, F., Marbach, M., Thoen, E., Elberg, A., & Larmarange, J. (2021). GGally: Extension to ’ggplot2’. https://CRAN.R-project.org/package=GGally
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Silbiger, N. J., Goodbody-Gringley, G., Bruno, J. F., & Putnam, H. M. (2019). Comparative thermal performance of the reef-building coral Orbicella franksi at its latitudinal range limits. Marine Biology, 166(10), 1–14. https://doi.org/10.1007/s00227-019-3573-6
Silk, J. B., Brosnan, S. F., Vonk, J., Henrich, J., Povinelli, D. J., Richardson, A. S., Lambeth, S. P., Mascaro, J., & Schapiro, S. J. (2005). Chimpanzees are indifferent to the welfare of unrelated group members. Nature, 437(7063, 7063), 1357–1359. https://doi.org/10.1038/nature04243
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press, USA. https://oxford.universitypressscholarship.com/view/10.1093/acprof:oso/9780195152968.001.0001/acprof-9780195152968
Slowikowski, K. (2022). ggrepel: Automatically position non-overlapping text labels with ’ggplot2’. https://CRAN.R-project.org/package=ggrepel
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Linde, A. V. D. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583–639. https://doi.org/10.1111/1467-9868.00353
Spiegelhalter, D., Thomas, A., Best, N., & Lunn, D. (2003). WinBUGS user manual. https://www.mrc-bsu.cam.ac.uk/wp-content/uploads/manual14.pdf
Stan Development Team. (2023). RStan: The R Interface to Stan. https://CRAN.R-project.org/package=rstan/vignettes/rstan.html
Stan Development Team. (2022a). Stan functions reference, Version 2.31. https://mc-stan.org/docs/functions-reference/
Stan Development Team. (2022b). Stan reference manual, Version 2.31. https://mc-stan.org/docs/reference-manual/index.html
Stan Development Team. (2022c). Stan user’s guide, Version 2.31. https://mc-stan.org/docs/stan-users-guide/index.html
Street, S. E., Navarrete, A. F., Reader, S. M., & Laland, K. N. (2017). Coevolution of cultural intelligence, extended life history, sociality, and brain size in primates. Proceedings of the National Academy of Sciences, 114(30), 7908–7914. https://doi.org/10.1073/pnas.1620734114
Subramanian, S. V., Kim, R., & Christakis, N. A. (2018). The “average” treatment effect: A construct ripe for retirement. A commentary on Deaton and Cartwright. Social Science & Medicine, 210, 77–82. https://doi.org/10.1016/j.socscimed.2018.04.027
Textor, J., van der Zander, B., & Ankan, A. (2021). dagitty: Graphical analysis of structural causal models. https://CRAN.R-project.org/package=dagitty
Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., & Ellison, G. T. (2016). Robust causal inference using directed acyclic graphs: The R package ’dagitty’. International Journal of Epidemiology, 45(6), 1887–1894. https://doi.org/10.1093/ije/dyw341
Thoen, E. (2022). dutchmasters [Manual]. https://github.com/EdwinTh/dutchmasters
Tufte, E. R. (2001). The visual display of quantitative information (Second Edition). Graphics Press. https://www.edwardtufte.com/tufte/books_vdqi
van Buuren, S. (2018). Flexible imputation of missing data (Second Edition). CRC Press. https://stefvanbuuren.name/fimd/
van Leeuwen, E. J. C., Cohen, E., Collier-Baker, E., Rapold, C. J., Schäfer, M., Schütte, S., & Haun, D. B. M. (2018). The development of human social learning across seven societies. Nature Communications, 9(1, 1), 2076. https://doi.org/10.1038/s41467-018-04468-2
Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., & Gelman, A. (2022). loo: Efficient leave-one-out cross-validation and WAIC for bayesian models. https://CRAN.R-project.org/package=loo/
Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413–1432. https://doi.org/10.1007/s11222-016-9696-4
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P.-C. (2019). Rank-normalization, folding, and localization: An improved \(\widehat{R}\) for assessing convergence of MCMC. https://arxiv.org/abs/1903.08008?
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (Fourth Edition). Springer. http://www.stats.ox.ac.uk/pub/MASS4
Vermeer, J. (1665). Girl with a pearl earring.
Volterra, V. (1926). Fluctuations in the abundance of a species considered mathematically. Nature, 118(2972, 2972), 558–560. https://doi.org/10.1038/118558a0
von Bertalanffy, L. (1934). Untersuchungen Über die Gesetzlichkeit des Wachstums. Wilhelm Roux’ Archiv für Entwicklungsmechanik der Organismen, 131(4), 613–652. https://doi.org/10.1007/BF00650112
Vonesh, J. R., & Bolker, B. M. (2005). Compensatory larval responses shift trade-offs associated with predator-induced hatching plasticity. Ecology, 86(6), 1580–1591. https://doi.org/10.1890/04-0535
Walker, K. (2022). Tigris: Load census TIGER/Line shapefiles [Manual]. https://github.com/walkerke/tigris
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11(116), 3571–3594. http://jmlr.org/papers/v11/watanabe10a.html
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063
Weber, S., & Bürkner, P.-C. (2022). Running brms models with within-chain parallelization. https://CRAN.R-project.org/package=brms/vignettes/brms_threading.html
Whitehouse, H., François, P., Savage, P. E., Currie, T. E., Feeney, K. C., Cioni, E., Purcell, R., Ross, R. M., Larson, J., Baines, J., ter Haar, B., Covey, A., & Turchin, P. (2019). Complex societies precede moralizing gods throughout world history. Nature, 568(7751, 7751), 226–229. https://doi.org/10.1038/s41586-019-1043-4
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://ggplot2-book.org/
Wickham, H. (2020). The tidyverse style guide. https://style.tidyverse.org/
Wickham, H. (2022). tidyverse: Easily install and load the ’tidyverse’. https://CRAN.R-project.org/package=tidyverse
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., Woo, K., Yutani, H., & Dunnington, D. (2022). ggplot2: Create elegant data visualisations using the grammar of graphics. https://CRAN.R-project.org/package=ggplot2
Wiecek, W., & Meager, R. (2022). baggr: Bayesian aggregate treatment effects [Manual]. https://CRAN.R-project.org/package=baggr
Wilke, C. O. (2019). Fundamentals of data visualization. https://clauswilke.com/dataviz/
Wilks, S. S. (1938). The large-sample distribution of the likelihood ratio for testing composite hypotheses. The Annals of Mathematical Statistics, 9(1), 60–62. https://doi.org/10.1214/aoms/1177732360
Williams, Donald R., Martin, S. R., Liu, S., & Rast, P. (2020). Bayesian multivariate mixed-effects location scale modeling of longitudinal relations among affective traits, states, and physical activity. European Journal of Psychological Assessment, 36(6), 981–997. https://doi.org/10.1027/1015-5759/a000624
Williams, Donald R., Martin, S. R., Liu, S., & Rast, P. (2021). Bayesian multivariate mixed-effects location scale modeling of longitudinal relations among affective traits, states, and physical activity. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000624
Williams, Donald R., Martin, S. R., & Rast, P. (2022). Putting the individual into reliability: Bayesian testing of homogeneous within-person variance in hierarchical models. Behavior Research Methods, 54(3), 1272–1290. https://doi.org/10.3758/s13428-021-01646-x
Williams, Donald R., Mulder, J., Rouder, J. N., & Rast, P. (2021). Beneath the surface: Unearthing within-person variability and mean relations with Bayesian mixed models. Psychological Methods, 26(1), 74. https://doi.org/10.1037/met0000270
Williams, Donald R., Rast, P., & Bürkner, P.-C. (2018). Bayesian meta-analysis with weakly informative prior distributions. https://doi.org/10.31234/osf.io/7tbrm
Williams, Donald R., Zimprich, D. R., & Rast, P. (2019). A Bayesian nonlinear mixed-effects location scale model for learning. Behavior Research Methods, 51(5), 1968–1986. https://doi.org/10.3758/s13428-019-01255-9
Wood, S. N. (2017a). Generalized additive models: An introduction with R (Second Edition). CRC Press. https://www.routledge.com/Generalized-Additive-Models-An-Introduction-with-R-Second-Edition/Wood/p/book/9781498728331
Wood, S. N. (2003). Thin-plate regression splines. Journal of the Royal Statistical Society (B), 65(1), 95–114. https://doi.org/10.1111/1467-9868.00374
Wood, S. N. (2004). Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association, 99(467), 673–686. https://doi.org/10.1198/016214504000000980
Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B), 73(1), 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x
Wood, S. N. (2017b). Generalized additive models: An introduction with R (2nd ed.). Chapman and Hall/CRC. https://www.routledge.com/Generalized-Additive-Models-An-Introduction-with-R-Second-Edition/Wood/p/book/9781498728331?utm_source=crcpress.com&utm_medium=referral
Wood, S. N. (2022). mgcv: Mixed GAM computation vehicle with automatic smoothness estimation. https://CRAN.R-project.org/package=mgcv
Wood, S. N., Pya, N., & Säfken, B. (2016). Smoothing parameter and model selection for general smooth models (with discussion). Journal of the American Statistical Association, 111, 1548–1575. https://doi.org/10.1080/01621459.2016.1180986
Xie, Y. (2022). bookdown: Authoring books and technical documents with R Markdown. https://CRAN.R-project.org/package=bookdown
Xie, Y., Allaire, J. J., & Grolemund, G. (2020). R markdown: The definitive guide. Chapman and Hall/CRC. https://bookdown.org/yihui/rmarkdown/
Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions (with discussion). Bayesian Analysis, 13(3), 917–1007. https://doi.org/10.1214/17-BA1091
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science : A Journal of the Association for Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393
Yu, G. (2020a). Data integration, manipulation and visualization of phylogenetic trees. https://yulab-smu.github.io/treedata-book/
Yu, G. (2020b). Using ggtree to visualize data on tree-like structures. Current Protocols in Bioinformatics, 69(1), e96. https://doi.org/10.1002/cpbi.96
Yu, G., Lam, T. T.-Y., Zhu, H., & Guan, Y. (2018). Two methods for mapping and visualizing associated data on phylogeny using ggtree. Molecular Biology and Evolution, 35(12), 3041–3043. https://doi.org/10.1093/molbev/msy194
Yu, G., Smith, D. K., Zhu, H., Guan, Y., & Lam, T. T.-Y. (2017). ggtree: An R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution, 8(1), 28–36. https://doi.org/10.1111/2041-210X.12628
Zhang, Y., & Yang, Y. (2015). Cross-validation for selecting a model selection procedure. Journal of Econometrics, 187(1), 95–112. https://doi.org/10.1016/j.jeconom.2015.02.006