Aden-Buie, G. (2020). ggpomological: Pomological plot theme for ggplot2 [Manual].
Agresti, A. (2015). Foundations of linear and generalized linear models. John Wiley & Sons.
Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In Selected papers of Hirotugu Akaike (pp. 199–213). Springer.
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.
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., & others. (2020). Risk of intracranial hemorrhage following intravenous tPA (Tissue-Type Plasminogen Activator) for acute stroke is low in children. Stroke, 51(2), 542–548.
Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567(7748), 305–307.
Angrist, J. D., & Keueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? The Quarterly Journal of Economics, 106(4), 979–1014.
Aono, Y. (2012). Long-term change in climate and floral phenophase. Chikyu Kankyo (Global Environment), 17.
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.
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.
Arnold, J. B. (2019). ggthemes: Extra themes, scales and geoms for ’ggplot2’.
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.
Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48(1), 5–37.
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.
Barrett, M. (2021a). An introduction to ggdag.
Barrett, M. (2021b). ggdag: Analyze and create elegant directed acyclic graphs.
Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2021). Modern data science with R (2nd edition). Taylor & Francis Group, LLC.
Betancourt, M. (2018). Bayes sparse regression.
Betancourt, M. (2017). Robust Gaussian processes in Stan.
Bickel, P. J., Hammel, E. A., & O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187(4175), 398–404.
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), 1515.
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.
Bryan, J., the STAT 545 TAs, & Hester, J. (2020). Happy Git and GitHub for the useR.
Bürkner, P.-C. (2021a). Define custom response distributions with brms.
Bürkner, P.-C. (2021b). Estimating distributional models with brms.
Bürkner, P.-C. (2021c). Estimating monotonic effects with brms.
Bürkner, P.-C. (2021d). Estimating multivariate models with brms.
Bürkner, P.-C. (2021e). Estimating non-linear models with brms.
Bürkner, P.-C. (2021f). Estimating phylogenetic multilevel models with brms.
Bürkner, P.-C. (2021g). Handle missing values with brms.
Bürkner, P.-C. (2021h). Parameterization of response distributions in brms.
Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28.
Bürkner, P.-C. (2018). Advanced Bayesian multilevel modeling with the R package brms. The R Journal, 10(1), 395–411.
Bürkner, P.-C. (2020). brms: Bayesian regression models using ’Stan.
Bürkner, P.-C. (2021i). brms reference manual, Version 2.15.0.
Bürkner, P.-C., & Charpentier, E. (2020). Modelling monotonic effects of ordinal predictors in Bayesian regression models. British Journal of Mathematical and Statistical Psychology.
Bürkner, P.-C., Gabry, J., Kay, M., & Vehtari, A. (2020). posterior: Tools for working with posterior distributions.
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.
Carvalho, C. M., Polson, N. G., & Scott, J. G. (2009). Handling sparsity via the horseshoe. Artificial Intelligence and Statistics, 73–80.
Casella, G., & George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician, 46(3), 167–174.
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.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences (Third Edition). Routledge.
Cover, T. M., & Thomas, J. A. (2006). Elements of information theory (2nd Edition). John Wiley & Sons.
Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29.
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.
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.
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.
Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. Springer.
Efron, B., & Morris, C. (1977). Stein’s paradox in statistics. Scientific American, 236(5), 119–127.
Enders, C. K. (2010). Applied missing data analysis. Guilford press.
Fernández i Marín, X. (2016). ggmcmc: Analysis of MCMC samples and Bayesian inference. Journal of Statistical Software, 70(9), 1–20.
Fernández i Marín, X. (2020). ggmcmc: Tools for analyzing MCMC simulations from Bayesian inference [Manual].
Gabry, J. (2021). Plotting MCMC draws using the bayesplot package.
Gabry, J., & Goodrich, B. (2020). rstanarm: Bayesian applied regression modeling via stan [Manual].
Gabry, J., & Mahr, T. (2021). bayesplot: Plotting for Bayesian models.
Gabry, J., & Modrák, M. (2021). Visual MCMC diagnostics using the bayesplot package.
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.
Garnier, S. (2018). viridis: Default color maps from ’matplotlib’ [Manual].
Gelman, A. (2005). Analysis of variance–Why it is more important than ever. Annals of Statistics, 33(1), 1–53.
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1(3), 515–534.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (Third Edition). CRC press.
Gelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2019). R-squared for Bayesian regression models. The American Statistician, 73(3), 307–309.
Gelman, A., & Greenland, S. (2019). Are confidence intervals better termed “uncertainty intervals?” BMJ, l5381.
Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press.
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.
Gelman, A., & Little, T. C. (1997). Postratification into many categories using hierarchical logistic regression. Survey Methodology, 23, 127–135.
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.
Gelman, A., Simpson, D., & Betancourt, M. (2017). The prior can often only be understood in the context of the likelihood. Entropy, 19(10), 555.
Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician, 60(4), 328–331.
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.
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.
Gohel, D. (2021a). flextable: Functions for tabular reporting [Manual].
Gohel, D. (2021b). Using the flextable R package.
Grafen, A., & Hails, R. (2002). Modern statistics for the life sciences. Oxford University Press.
Grantham, N. (2019). ggdark: Dark mode for ’ggplot2’ themes [Manual].
Grolemund, G., & Wickham, H. (2017). R for data science. O’Reilly.
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.
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.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media.
Hauer, E. (2004). The harm done by tests of significance. Accident Analysis & Prevention, 36(3), 495–500.
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.
Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications.
Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press.
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.
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).
Henderson, E. (2020). ghibli: Studio ghibli colour palettes [Manual].
Henry, L., & Wickham, H. (2020). purrr: Functional programming tools.
Hewitt, C. G. (1921). The conservation of the wild life of Canada. Charles Scribner’s Sons.
Hinde, K., & Milligan, L. A. (2011). Primate milk: Proximate mechanisms and ultimate perspectives. Evolutionary Anthropology: Issues, News, and Reviews, 20(1), 9–23.
Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change (1 edition). Routledge.
Howell, N. (2001). Demography of the dobe! Kung (2nd Edition). Routledge.
Howell, N. (2010). Life histories of the Dobe! Kung: Food, fatness, and well-being over the life span (Vol. 4). Univ of California Press.
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.
Kahle, D., & Stamey, J. (2017). invgamma: The inverse gamma distribution [Manual].
Kale, A., Kay, M., & Hullman, J. (2020). Visual reasoning strategies for effect size judgments and decisions. IEEE Transactions on Visualization and Computer Graphics.
Kay, M. (2020a). Extracting and visualizing tidy draws from brms models.
Kay, M. (2020b). Marginal distribution of a single correlation from an LKJ distribution.
Kay, M. (2020c). tidybayes: Tidy data and ’geoms’ for Bayesian models.
Kennedy, L., & Gelman, A. (2020). Know your population and know your model: Using model-based regression and poststratification to generalize findings beyond the observed sample. arXiv:1906.11323 [Stat].
Kievit, R., Frankenhuis, W. E., Waldorp, L., & Borsboom, D. (2013). Simpson’s paradox in psychological science: A practical guide. Frontiers in Psychology, 4.
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.
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.
Kolczynska, M., Bürkner, P.-C., Kennedy, L., & Vehtari, A. (2020). Trust in state institutions in Europe, 1989-2019. SocArXiv.
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.
Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79–86.
Kurz, A. S. (2020a). Statistical rethinking with brms, ggplot2, and the tidyverse (version 1.2.0).
Kurz, A. S. (2020b). Applied Longitudinal Data Analysis in brms and the tidyverse (version 0.0.1).
Kurz, A. S. (2020c). Doing Bayesian data analysis in brms and the tidyverse (version 0.3.0).
Kurz, A. S. (2019). Recoding Introduction to mediation, moderation, and conditional process analysis (version 1.1.0).
Kurz, A. S., DeBeer, B. B., Kimbrel, N. A., Morissette, S. B., & Meyer, E. C. (2019, October). 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.
Legler, J., & Roback, P. (2019). Broadening your statistical horizons: Generalized linear models and multilevel models.
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.
Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis with missing data. John Wiley & Sons.
Lotka, A. J. (1925). Principles of physical biology. Waverly.
Matamoros, I. A. A., & Torres, C. A. C. (2020). varstan: An R package for Bayesian analysis of structured time series models with Stan. arXiv:2005.10361 [Stat].
Matejka, J., & Fitzmaurice, G. (2017). Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing.
McElreath, R. (2020a). Statistical rethinking: A Bayesian course with examples in R and Stan (Second Edition). CRC Press.
McElreath, R. (2015). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press.
McElreath, R. (2020b). rethinking R package.
Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66(1), 195–244.
Merkle, E. C., & Rosseel, Y. (2018). blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software, 85(4), 1–30.
Merkle, E. C., Rosseel, Y., & Goodrich, B. (2020). blavaan: Bayesian latent variable analysis.
Müller, K., & Wickham, H. (2020). tibble: Simple data frames.
Navarro, D. (2019). Learning statistics with R.
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.
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., & others. (2018). Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. New England Journal of Medicine, 378(1), 11–21.
Nowosad, J. (2019). rcartocolor: ’CARTOColors’ palettes.
Nunn, N., & Puga, D. (2012). Ruggedness: The blessing of bad geography in Africa. Review of Economics and Statistics, 94(1), 20–36.
Paananen, T., Bürkner, P.-C., Vehtari, A., & Gabry, J. (2020). Avoiding model refits in leave-one-out cross-validation with moment matching.
Paananen, T., Piironen, J., Bürkner, P.-C., & Vehtari, A. (2020). Implicitly adaptive importance sampling. arXiv:1906.08850 [Stat].
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. (2020). ape: Analyses of phylogenetics and evolution [Manual].
Paradis, E., & Schliep, K. (2019). ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 35, 526–528.
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.
Pedersen, T. L. (2019). patchwork: The composer of plots.
Peng, R. D. (2019). R programming for data science.
Peng, R. D., Kross, S., & Anderson, B. (2017). Mastering software development in {}R{}.
Pivot data from wide to long pivot_longer. (2020).
Pivoting. (2020).
Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Working Papers, 8.
R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
R Library Contrast Coding Systems for categorical variables. (n.d.). In UCLA: Statistical Consulting Group. Retrieved October 14, 2020, from
Ram, K., & Wickham, H. (2018). wesanderson: A Wes Anderson palette generator [Manual].
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.
Revelle, W. (2020). psych: Procedures for psychological, psychometric, and personality research.
Ripley, B. (2019). MASS: Support functions and datasets for venables and ripley’s MASS.
Robert, C., & Casella, G. (2011). A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data. Statistical Science, 26(1), 102–115.
Robinson, D., & Hayes, A. (2020). broom: Convert statistical analysis objects into tidy tibbles [Manual].
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.
Rubin, Donald B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91(434), 473–489.
Rubin, Donald B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.
Rubin, Donald B. (1987). Multiple imputation for nonresponse in surveys. John Wiley & Sons Inc.
Rudis, B. (2020). statebins: Create united states uniform cartogram heatmaps [Manual].
Rudis, B., Ross, N., & Garnier, S. (2018). The viridis color palettes.
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.
Schloerke, B., Crowley, J., Di Cook, Briatte, F., Marbach, M., Thoen, E., Elberg, A., & Larmarange, J. (2020). GGally: Extension to ’ggplot2’.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423.
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.
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), 1357–1359.
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.
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press, USA.
Slowikowski, K. (2020). ggrepel: Automatically position non-overlapping text labels with ’ggplot2’.
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.
Spiegelhalter, D., Thomas, A., Best, N., & Lunn, D. (2003). WinBUGS user manual.
Stan Development Team. (2020). RStan: The R interface to Stan.
Stan Development Team. (2021a). Stan functions reference.
Stan Development Team. (2021b). Stan reference manual, Version 2.26.
Stan Development Team. (2021c). Stan user’s guide, Version 2.26.
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.
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.
Textor, J., & der Zander, B. van. (2016). dagitty: Graphical analysis of structural causal models.
Thoen, E. (2019). dutchmasters [Manual].
Tufte, E. R. (2001). The visual display of quantitative information (Second Edition). Graphics Press.
Urban Institute. (2020). urbnmapr: State and county maps with Alaska and Hawaii.
van Buuren, S. (2018). Flexible imputation of missing data (Second Edition). CRC Press.
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), 2076.
Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., & Gelman, A. (2019). loo: Efficient leave-one-out cross-validation and WAIC for bayesian models.
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.
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. arXiv Preprint arXiv:1903.08008.
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (Fourth Edition). Springer.
Vermeer, J. (1665). Girl with a pearl earring.
Volterra, V. (1926). Fluctuations in the abundance of a species considered mathematically. Nature, 118(2972), 558–560.
von Bertalanffy, L. (1934). Untersuchungen Über die Gesetzlichkeit des Wachstums. Wilhelm Roux’ Archiv für Entwicklungsmechanik Der Organismen, 131(4), 613–652.
Vonesh, J. R., & Bolker, B. M. (2005). Compensatory larval responses shift trade-offs associated with predator-induced hatching plasticity. Ecology, 86(6), 1580–1591.
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133.
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.
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.
Weber, S., & Bürkner, P.-C. (2021). Running brms models with within-chain parallelization.
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), 226–229.
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York.
Wickham, H. (2019). tidyverse: Easily install and load the ’tidyverse’.
Wickham, H. (2020). The tidyverse style guide.
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.
Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., Woo, K., Yutani, H., & Dunnington, D. (2020). ggplot2: Create elegant data visualisations using the grammar of graphics.
Wiecek, W., & Meager, R. (2020). baggr: Bayesian aggregate treatment effects [Manual].
Wilke, C. O. (2019). Fundamentals of data visualization.
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.
Williams, D. R., Liu, S., Martin, S. R., & Rast, P. (2019). Bayesian multivariate mixed-effects location scale modeling of longitudinal relations among affective traits, states, and physical activity.
Williams, D. R., Martin, S. R., & Rast, P. (2019). Putting the individual into reliability: Bayesian testing of homogeneous within-person variance in hierarchical models.
Williams, D. R., Rast, P., & Bürkner, P.-C. (2018). Bayesian meta-analysis with weakly informative prior distributions.
Williams, D. R., Rouder, J., & Rast, P. (2019). Beneath the surface: Unearthing within-Person variability and mean relations with Bayesian mixed models.
Williams, D. R., Zimprich, D. R., & Rast, P. (2019). A Bayesian nonlinear mixed-effects location scale model for learning. Behavior Research Methods, 51(5), 1968–1986.
Wood, S. N. (2017a). Generalized additive models: An introduction with R (Second Edition). CRC Press.
Wood, S. N. (2003). Thin-plate regression splines. Journal of the Royal Statistical Society (B), 65(1), 95–114.
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.
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.
Wood, S. N. (2017b). Generalized additive models: An introduction with r (Second). Chapman and Hall/CRC.
Wood, S. N. (2019). mgcv: Mixed GAM computation vehicle with automatic smoothness estimation.
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.
Xie, Y. (2020). bookdown: Authoring books and technical documents with R Markdown.
Xie, Y., Allaire, J. J., & Grolemund, G. (2020). R markdown: The definitive guide. Chapman and Hall/CRC.
Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions (with discussion). Bayesian Analysis, 13(3), 917–1007.
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.
Yu, G. (2020a). Data integration, manipulation and visualization of phylogenetic trees.
Yu, G. (2020b). Using ggtree to visualize data on tree-like structures. Current Protocols in Bioinformatics, 69(1), e96.
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.
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.
Zhang, Y., & Yang, Y. (2015). Cross-validation for selecting a model selection procedure. Journal of Econometrics, 187(1), 95–112.