Aczel, B., Hoekstra, R., Gelman, A., Wagenmakers, E.-J., Klugkist, I. G., Rouder, J. N., Vandekerckhove, J., Lee, M. D., Morey, R. D., Vanpaemel, W., Dienes, Z., & van Ravenzwaaij, D. (2020). Discussion points for Bayesian inference. Nature Human Behaviour, 1–3.
Agresti, A. (2015). Foundations of linear and generalized linear models. John Wiley & Sons.
Allan, A., Cook, D., Gayler, R., Kirk, H., Peng, R., & Saber, E. (2021). ochRe: Australia-themed colour palettes [Manual].
Arnold, J. B. (2021). 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, 27(1), 166.
Attali, D., & Baker, C. (2022). ggExtra: Add marginal histograms to ’ggplot2’, and more ’ggplot2’ enhancements.
Auguie, B. (2017). gridExtra: Miscellaneous functions for "grid" graphics.
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. (2021). lme4: Linear mixed-effects models using Eigen’ and S4.
Bayes, T. (1763). LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S. Philosophical Transactions of the Royal Society of London, 53, 370–418.
BibTeX. (2020).
Bliss, C. I. (1934). The method of probits. Science.
Bolger, N., Zee, K. S., Rossignac-Milon, M., & Hassin, R. R. (2019). Causal processes in psychology are heterogeneous. Journal of Experimental Psychology: General, 148(4), 601–618.
Braumoeller, B. F. (2004). Hypothesis testing and multiplicative interaction terms. International Organization, 58(4), 807–820.
Bryan, J., the STAT 545 TAs, & Hester, J. (2020). Happy Git and GitHub for the useR.
Bürkner, P.-C. (2020). Bayesian item response modeling in R with brms and Stan.
Bürkner, P.-C. (2022a). Define custom response distributions with brms.
Bürkner, P.-C. (2022b). Estimating distributional models 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). 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. (2022f). brms reference manual, Version 2.17.0.
Bürkner, P.-C. (2022g). brms: Bayesian regression models using ’Stan.
Bürkner, P.-C., Gabry, J., Kay, M., & Vehtari, A. (2021). posterior: Tools for working with posterior distributions [Manual].
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.
Campbell, H., & Gustafson, P. (2021). Re: Linde et al.(2021) factor, HDI-ROPE and frequentist equivalence testing are actually all equivalent.
Carifio, J., & Perla, R. (2008). Resolving the 50-year debate around using and misusing Likert scales. Medical Education, 42(12), 1150–1152.
Carifio, J., & Perla, R. J. (2007). Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes. Journal of Social Sciences, 3(3), 106–116.
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.
Chandramouli, S. H., & Shiffrin, R. M. (2019). Commentary on Gronau and Wagenmakers. Computational Brain & Behavior, 2(1), 12–21.
Chen, M.-H., He, X., Shao, Q.-M., & Xu, H. (2003). A Monte Carlo gap test in computing HPD regions. In Development of Modern Statistics and Related Topics: Vols. Volume 1 (pp. 38–52). World Scientific.
Chen, M.-H., & Shao, Q.-M. (1999). Monte Carlo estimation of Bayesian credible and HPD intervals. Journal of Computational and Graphical Statistics, 8(1), 69–92.
Chung, Y., Rabe-Hesketh, S., Dorie, V., Gelman, A., & Liu, J. (2013). A nondegenerate penalized likelihood estimator for variance parameters in multilevel models. Psychometrika, 78(4), 685–709.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd Edition). Routledge.
Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. Routledge.
Dale, A. I. (2012). A history of inverse probability: From Thomas Bayes to Karl Pearson. Springer Science & Business Media.
Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222.
Eckhardt, R. (1987). Stan Ulam, John von Neumann and the Monte Carlo method. Argonne, USA.
Efron, B., & Morris, C. (1977). Stein’s paradox in statistics. Scientific American, 236(5), 119–127.
Ellison., S. L. R. (2018). metRology: Support for metrological applications.
Enders, C. (2013). Centering predictors and contextual effects. In M. Scott, J. Simonoff, & B. Marx (Eds.), The SAGE Handbook of Multilevel Modeling (pp. 89–108). SAGE Publications Ltd.
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121.
Fernandes, M., Walls, L., Munson, S., Hullman, J., & Kay, M. (2018). Uncertainty displays using quantile dotplots or CDFs improve transit decision-making. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–12). Association for Computing Machinery.
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].
Firke, S. (2020). janitor: Simple tools for examining and cleaning dirty data.
Fisher, R. A. (1925). Statistical methods for research workers, 11th ed. rev. Edinburgh.
Gabry, J. (2022). Graphical posterior predictive checks using the bayesplot package.
Gabry, J., & Mahr, T. (2022). bayesplot: Plotting for Bayesian models.
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. (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., Hill, J., & Yajima, M. (2012). Why we (usually) don’t have to worry about multiple comparisons. Journal of Research on Educational Effectiveness, 5(2), 189–211.
Grolemund, G., & Wickham, H. (2017). R for data science. O’Reilly.
Gronau, Q. F., & Wagenmakers, E.-J. (2019a). Limitations of Bayesian leave-one-out cross-validation for model selection. Computational Brain & Behavior, 2(1), 1–11.
Gronau, Q. F., & Wagenmakers, E.-J. (2019b). Rejoinder: More limitations of Bayesian leave-one-out cross-validation. Computational Brain & Behavior, 2(1), 35–47.
Guber, L., Deborah. (1999). Getting what you pay for: The debate over equity in public school expenditures. Journal of Statistics Education, 7(2).
Hamaker, E. L. (2012). Why researchers should think "within-person": A paradigmatic rationale. In Handbook of research methods for studying daily life (pp. 43–61). The Guilford Press.
Hanley, J., A, & Shapiro, S., H. (1994). Sexual activity and the lifespan of male fruitflies: A dataset that gets attention. Journal of Statistics Education, 2(1), null.
Henry, L., & Wickham, H. (2020). purrr: Functional programming tools.
Heyns, E. (2020). Better BibTeX for zotero.
Hocking, T. D. (2021). Directlabels: Direct labels for multicolor plots [Manual].
Hokusai, K. (1820–1831). The great wave off Kanagawa.
Hugh-Jones, D. (2020). santoku: A versatile cutting tool.
Hyndman, R. J. (1996). Computing and graphing highest density regions. The American Statistician, 50(2), 120–126.
Jean, J. (2009). RIFT SCULL.
Jeffreys, H. (1961). Theory of probability. Oxford University Press.
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.
Kay, M. (2022). Slab + interval stats and geoms.
Kay, M. (2021). Extracting and visualizing tidy draws from brms models.
Kay, M. (2020). tidybayes: Tidy data and ’geoms’ for Bayesian models.
Kay, M., Kola, T., Hullman, J. R., & Munson, S. A. (2016). When (ish) is my bus? User-centered visualizations of uncertainty in everyday, mobile predictive systems. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 5092–5103.
Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137.
Klein, O., Hardwicke, T. E., Aust, F., Breuer, J., Danielsson, H., Hofelich Mohr, A., IJzerman, H., Nilsonne, G., Vanpaemel, W., & Frank, M. C. (2018). A practical guide for transparency in psychological science. Collabra: Psychology, 4(1), 1–15.
Kolmogorov, A. N., & Bharucha-Reid, A. T. (1956). Foundations of the theory of probability: Second English Edition. Chelsea Publishing Company.
Kruschke, J. K. (2013). Posterior predictive checks can and should be Bayesian: Comment on Gelman and Shalizi, Philosophy and the practice of Bayesian statistics.’ British Journal of Mathematical and Statistical Psychology, 66(1), 45–56.
Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
Kruschke, J. K. (2021). Bayesian analysis reporting guidelines. Nature Human Behaviour, 5(10), 1282–1291.
Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25(1), 178–206.
Kurz, A. S. (2021). Statistical rethinking with brms, ggplot2, and the tidyverse: Second Edition (version 0.2.0).
Kurz, A. S. (2020). Statistical rethinking with brms, ggplot2, and the tidyverse (version 1.2.0).
Lakens, D., & Delacre, M. (2018). Equivalence testing and the second generation p-value.
Lakens, D., McLatchie, N., Isager, P. M., Scheel, A. M., & Dienes, Z. (2020). Improving inferences about null effects with Bayes factors and equivalence tests. The Journals of Gerontology: Series B, 75(1), 45–57.
Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269.
Lawlor, J. (2020). PNWColors: Color palettes inspired by nature in the US Pacific Northwest [Manual].
Lee, M. D., & Webb, M. R. (2005). Modeling individual differences in cognition. Psychonomic Bulletin & Review, 12(4), 605–621.
Legler, J., & Roback, P. (2019). Broadening your statistical horizons: Generalized linear models and multilevel models.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22 140, 55–55.
Linde, M., Tendeiro, J. N., Selker, R., Wagenmakers, E.-J., & van Ravenzwaaij, D. (2021). Decisions about equivalence: A comparison of TOST, HDI-ROPE, and the Bayes factor. Psychological Methods.
Littlefield, T. (2020). lisa: Color palettes from color lisa [Manual].
Liu, C. C., & Aitkin, M. (2008). Bayes factors: Prior sensitivity and model generalizability. Journal of Mathematical Psychology, 52(6), 362–375.
Lucas, T. (2016). palettetown: Use Pokemon inspired colour palettes [Manual].
Luce, R. D. (2012). Individual choice behavior: A theoretical analysis. Courier Corporation.
Luce, R. D. (2008). Luce’s choice axiom. Scholarpedia, 3(12), 8077.
MacKay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge University Press.
Martone, M. E., Garcia-Castro, A., & VandenBos, G. R. (2018). Data sharing in psychology. The American Psychologist, 73(2), 111–125.
McElreath, R. (2020). 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.
McGrayne, S. B. (2011). The theory that would not die: How Bayes’ rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy. Yale University Press.
McWhite, C. D., & Wilke, C. O. (2021). colorblindr: Simulate colorblindness in R figures [Manual].
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. (2021). blavaan: Bayesian latent variable analysis.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.
Miller, D. L. (2021). beyonce: Beyoncé colour palettes for R.
Miller, J. (2009). What is the probability of replicating a statistically significant effect? Psychonomic Bulletin & Review, 16(4), 617–640.
Müller, K., & Wickham, H. (2020). tibble: Simple data frames.
Nakagawa, S., & Foster, T. M. (2004). The case against retrospective statistical power analyses with an introduction to power analysis. Acta Ethologica, 7(2), 103–108.
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.
Neal, R. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones, & X.-L. Meng (Eds.), Handbook of Markov chain Monte Carlo (pp. 116–162). London, United Kingdom: Chapman & Hall/CRC Press.
Neal, R. M. (1994). An improved acceptance procedure for the hybrid Monte Carlo algorithm. Journal of Computational Physics, 111(1), 194–203.
Nelder, J. A., & Wedderburn, R. W. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.
Nicenboim, B., Schad, D., & Vasishth, S. (2021). An introduction to Bayesian data analysis for cognitive science.
Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632.
O’Keefe, D. J. (2007). Brief report: Post hoc power, observed power, a priori power, retrospective power, prospective power, achieved power: Sorting out appropriate uses of statistical power analyses. Communication Methods and Measures, 1(4), 291–299.
Pedersen, Thomas Lin. (n.d.). Draw polygons with expansion/contraction and/or rounded corners geom_shape. Retrieved September 11, 2020, from
Pedersen, Thomas L. (2020a). Adding annotation and style.
Pedersen, Thomas Lin. (2020). patchwork: The composer of plots.
Pedersen, Thomas L. (2020b). Plot assembly.
Pedersen, Thomas Lin. (2021). ggforce: Acceleratingggplot2 [Manual].
Pedersen, Thomas Lin, & Crameri, F. (2021). scico: Colour palettes based on the scientific colour-maps [Manual].
Pek, J., & Flora, D. B. (2018). Reporting effect sizes in original psychological research: A discussion and tutorial. Psychological Methods, 23(2), 208.
Peng, R. D. (2020). R programming for data science.
Piironen, J., & Vehtari, A. (2017). Sparsity information and regularization in the horseshoe and other shrinkage priors. Electronic Journal of Statistics, 11(2), 5018–5051.
Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News, 6(1), 7–11.
Plummer, M., Best, N., Cowles, K., Vines, K., Sarkar, D., Bates, D., Almond, R., & Magnusson, A. (2020). coda: Output analysis and diagnostics for MCMC [Manual].
R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Revelle, W. (2022). psych: Procedures for psychological, psychometric, and personality research.
Ripley, B. (2021). MASS: Support functions and datasets for venables and Ripley’s MASS.
Rosa, L., Rosa, E., Sarner, L., & Barrett, S. (1998). A close look at therapeutic touch. JAMA, 279(13), 1005–1010.
Rouder, J. N. (2016). The what, why, and how of born-open data. Behavior Research Methods, 48(3), 1062–1069.
Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374.
Roy Rosenzweig Center for History and New Media. (2020). Zotero.
Schiettekatte, N. M. D., Brandl, S. J., & Casey, J. M. (2022). fishualize: Color palettes based on fish species [Manual].
Schloerke, B., Crowley, J., Di Cook, Briatte, F., Marbach, M., Thoen, E., Elberg, A., & Larmarange, J. (2021). GGally: Extension to ’ggplot2’.
Skinner, B. F. (1956). A case history in scientific method. American Psychologist, 11(5), 221–233.
Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28(1), 9–12.
Stan Development Team. (2022a). Accessing the contents of a stanfit object.
Stan Development Team. (2022b). Stan reference manual, Version 2.29.
Stan Development Team. (2022c). Stan user’s guide, Version 2.29.
Steidl, R. J., Hayes, J. P., & Schauber, E. (1997). Statistical power analysis in wildlife research. The Journal of Wildlife Management, 61(2), 270.
Sun, S., Pan, W., & Wang, L. L. (2011). Rethinking observed power: Concept, practice, and implications. Methodology, 7(3), 81–87.
Thomas, L. (1997). Retrospective power analysis. Conservation Biology, 11(1), 276–280.
Vanpaemel, W. (2010). Prior sensitivity in theory testing: An apologia for the Bayes factor. Journal of Mathematical Psychology, 54(6), 491–498.
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 $\widehatR$ for assessing convergence of MCMC (with Discussion). Bayesian Analysis, 16(2), 667–718.
Vehtari, A., Simpson, D. P., Yao, Y., & Gelman, A. (2019). Limitations of Limitations of Bayesian leave-one-out cross-validation for model selection.” Computational Brain & Behavior, 2(1), 22–27.
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (Fourth Edition). Springer.
Wagenmakers, E.-J. (2007). A practical solution to the pervasive problems of p values. Psychonomic Bulletin & Review, 14(5), 779–804.
Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage method. Cognitive Psychology, 60(3), 158–189.
Weber, S., & Bürkner, P.-C. (2022). Running brms models with within-chain parallelization.
Wetzels, R., Grasman, R. P. P. P., & Wagenmakers, E.-J. (2012). A default Bayesian hypothesis test for ANOVA designs. The American Statistician, 66(2), 104–111.
Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E.-J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t tests. Perspectives on Psychological Science, 6(3), 291–298.
Wickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21(12), 1–20.
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York.
Wickham, H. (2019). stringr: Simple, consistent wrappers for common string operations.
Wickham, H. (2020a). cubelyr: A data cube ’dplyr’ backend.
Wickham, H. (2020b). forcats: Tools for working with categorical variables (factors).
Wickham, H. (2020c). reshape2: Flexibly reshape data: A reboot of the reshape package.
Wickham, H. (2020d). The tidyverse style guide.
Wickham, H. (2021). 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. (2021). 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.
Wickham, H., & Henry, L. (2020). tidyr: Tidy messy data.
Wickham, H., Hester, J., & Francois, R. (2018). readr: Read rectangular text data.
Wilke, C. O. (2020a). Themes.
Wilke, C. O. (2019). Fundamentals of data visualization.
Wilke, C. O. (2020b). cowplot: Streamlined plot theme and plot annotations for ggplot2 [Manual].
Wilke, C. O. (2021). ggridges: Ridgeline Plots in ’ggplot2.
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., Zimprich, D. R., & Rast, P. (2019). A Bayesian nonlinear mixed-effects location scale model for learning. Behavior Research Methods, 51(5), 1968–1986.
Xie, Y. (2022). Bookdown: Authoring books and technical documents with R Markdown. Chapman and Hall/CRC.
Xie, Y., Allaire, J. J., & Grolemund, G. (2022). 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.
Zhu, M., & Lu, A. Y. (2004). The counter-intuitive non-informative prior for the Bernoulli family. Journal of Statistics Education, 12(2), 3.