Agresti, A. (2015). Foundations of linear and generalized linear models. John Wiley & Sons.
Arnold, J. B. (2021). ggthemes: Extra themes, scales and geoms for ’ggplot2’.
Attali, D., & Baker, C. (2022). ggExtra: Add marginal histograms to ’ggplot2’, and more ’ggplot2’ enhancements.
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.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48.
Bates, D., Maechler, M., Bolker, B., & Steven Walker. (2022). lme4: Linear mixed-effects models using Eigen’ and S4.
Betancourt, M. (2018). Bayes sparse regression.
BibTeX. (2020).
Bickel, P. J., Hammel, E. A., & O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187(4175), 398–404.
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. (2022a). Estimating distributional models with brms.
Bürkner, P.-C. (2022b). Define custom response distributions with brms.
Bürkner, P.-C. (2022c). Estimating multivariate models with brms.
Bürkner, P.-C. (2022d). Estimating non-linear models with brms.
Bürkner, P.-C. (2022e). Estimating phylogenetic multilevel models with brms.
Bürkner, P.-C. (2022f). Handle missing values with brms.
Bürkner, P.-C. (2022g). 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. (2022h). brms reference manual, Version 2.18.0.
Bürkner, P.-C. (2022i). brms: Bayesian regression models using ’Stan.
Bürkner, P.-C., Gabry, J., Kay, M., & Vehtari, A. (2022). 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.
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1).
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.
Clarke, E., & Sherrill-Mix, S. (2017). ggbeeswarm: Categorical scatter (violin point) plots [Manual].
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.
Efron, B., & Morris, C. (1977). Stein’s paradox in statistics. Scientific American, 236(5), 119–127.
Enders, C. K. (2022). Applied missing data analysis (Second Edition). 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. (2021). ggmcmc: Tools for analyzing MCMC simulations from Bayesian inference [Manual].
Gabry, J. (2022a). loo reference manual, Version 2.5.1.
Gabry, J. (2022b). Plotting MCMC draws using the bayesplot package.
Gabry, J., & Goodrich, B. (2022). rstanarm: Bayesian applied regression modeling via stan [Manual].
Gabry, J., & Mahr, T. (2022). bayesplot: Plotting for Bayesian models.
Gabry, J., & Modrák, M. (2022). 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. (2021). viridis: Default color maps from ’matplotlib’ [Manual].
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., & 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, 10), 555.
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.
Gershoff, Elizabeth T. (2013). Spanking and child development: We know enough now to stop hitting our children. Child Development Perspectives, 7(3), 133–137.
Gershoff, Elizabeth T., & Grogan-Kaylor, A. (2016). Spanking and child outcomes: Old controversies and new meta-analyses. Journal of Family Psychology, 30(4), 453.
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.
Hauer, E. (2004). The harm done by tests of significance. Accident Analysis & Prevention, 36(3), 495–500.
Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press.
Henderson, E. (2022). ghibli: Studio ghibli colour palettes [Manual].
Henry, L., & Wickham, H. (2020). purrr: Functional programming tools.
Heyns, E. (2020). Better BibTeX for zotero.
Hinde, K., & Milligan, L. A. (2011). Primate milk: Proximate mechanisms and ultimate perspectives. Evolutionary Anthropology: Issues, News, and Reviews, 20(1), 9–23.
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.
Kahle, D., & Stamey, J. (2017). invgamma: The inverse gamma distribution [Manual].
Kallioinen, N., Bürkner, P.-C., Paananen, T., & Vehtari, A. (2022). priorsense: Prior diagnostics and sensitivity analysis [Manual].
Kallioinen, N., Paananen, T., Bürkner, P.-C., & Vehtari, A. (2021). Detecting and diagnosing prior and likelihood sensitivity with power-scaling. arXiv.
Kay, M. (2021). Extracting and visualizing tidy draws from brms models.
Kay, M. (2020). Marginal distribution of a single correlation from an LKJ distribution.
Kay, M. (2022). tidybayes: Tidy data and ’geoms’ for Bayesian models.
Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137.
Kievit, R., Frankenhuis, W. E., Waldorp, L., & Borsboom, D. (2013). Simpson’s paradox in psychological science: A practical guide. Frontiers in Psychology, 4.
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.
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. (2023a). Doing Bayesian data analysis in brms and the tidyverse (Version 1.1.0).
Kurz, A. S. (2023b). Statistical Rethinking with brms, ggplot2, and the tidyverse: Second Edition (version 0.4.0).
Legler, J., & Roback, P. (2019). Broadening your statistical horizons: Generalized linear models and multilevel models.
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.
McHenry, H. M., & Coffing, K. (2000). Australopithecus to Homo: Transformations in body and mind. Annual Review of Anthropology, 29(1), 125–146.
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.
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. (2022). blavaan: Bayesian latent variable analysis.
Müller, K., & Wickham, H. (2022). 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.
Nicenboim, B., Schad, D., & Vasishth, S. (2022). An introduction to Bayesian data analysis for cognitive science.
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.
Pedersen, T. L. (2022). patchwork: The composer of plots.
Peng, R. D. (2022). R programming for data science.
Peng, R. D., Kross, S., & Anderson, B. (2017). Mastering software development in {}R{}.
R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Ram, K., & Wickham, H. (2018). wesanderson: A Wes Anderson palette generator [Manual].
Ripley, B. (2022). 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., & Couch, S. (2022). broom: Convert statistical objects into tidy tibbles [Manual].
Roy Rosenzweig Center for History and New Media. (2020). Zotero.
Rudis, B. (2020). hrbrthemes: Additional themes, theme components and utilities for ’Ggplot2’ [Manual].
Rudis, B., Ross, N., & Garnier, S. (2018). The viridis color palettes.
Schloerke, B., Crowley, J., Di Cook, Briatte, F., Marbach, M., Thoen, E., Elberg, A., & Larmarange, J. (2021). GGally: Extension to ’ggplot2’.
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.
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.
Slowikowski, K. (2022). ggrepel: Automatically position non-overlapping text labels with ’ggplot2’.
Stan Development Team. (2023). RStan: The R Interface to Stan.
Stan Development Team. (2022). Stan functions reference, Version 2.31.
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.
Thoen, E. (2022). dutchmasters [Manual].
Tufte, E. R. (2001). The visual display of quantitative information (Second Edition). Graphics Press.
UNICEF. (2014). Hidden in plain sight: A statistical analysis of violence against children.
van Buuren, S. (2018). Flexible imputation of missing data (Second Edition). CRC Press.
Van der Lee, R., & Ellemers, N. (2015). Gender contributes to personal research funding success in The Netherlands. Proceedings of the National Academy of Sciences, 112(40), 12349–12353.
Vehtari, A., & Gabry, J. (2022a). Using the loo package (Version \(>\)= 2.0.0).
Vehtari, A., & Gabry, J. (2022b, March 23). Bayesian stacking and pseudo-BMA weights using the loo package.
Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., & Gelman, A. (2022). 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. (2021). Rank-normalization, folding, and localization: An improved \(\widehat{R}\) for assessing convergence of MCMC (with Discussion). Bayesian Analysis, 16(2), 667–718.
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (Fourth Edition). Springer.
Vermeer, J. (1665). Girl with a pearl earring.
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48.
Viechtbauer, W. (2022). metafor: Meta-analysis package for R [Manual].
Volker, B., & Steenbeek, W. (2015). No evidence that gender contributes to personal research funding success in The Netherlands: A reaction to van der Lee and Ellemers. Proceedings of the National Academy of Sciences, 112(51), E7036–E7037.
Vonesh, J. R., & Bolker, B. M. (2005). Compensatory larval responses shift trade-offs associated with predator-induced hatching plasticity. Ecology, 86(6), 1580–1591.
Walker, K. (2022). Tigris: Load Census TIGER/Line shapefiles.
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. 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.
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York.
Wickham, H. (2020). The tidyverse style guide.
Wickham, H. (2022). tidyverse: Easily install and load the ’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.
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.
Wickham, H., François, R., Henry, L., & Müller, K. (2020). dplyr: A grammar of data manipulation.
Wilke, C. O. (2019). Fundamentals of data visualization.
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.
Williams, Donald R., Rast, P., & Bürkner, P.-C. (2018). Bayesian meta-analysis with weakly informative prior distributions.
Winerman, L. (2017). Trends report: Psychologists embrace open science. Monitor on Psychology, 48(10).
Xie, Y. (2022). 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.