References
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. https://doi.org/10.1038/s41562-019-0807-z
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
Allan, A., Cook, D., Gayler, R., Kirk, H., Peng, R., & Saber, E.
(2021). ochRe: Australia-themed colour palettes [Manual]. https://github.com/ropenscilabs/ochRe
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, 27(1), 166. https://doi.org/10.1037/a0029508
Attali, D., & Baker, C. (2022). ggExtra: Add marginal histograms to
’ggplot2’, and more ’ggplot2’ enhancements. https://CRAN.R-project.org/package=ggExtra
Auguie, B. (2017). gridExtra:
Miscellaneous functions for "grid" graphics. https://CRAN.R-project.org/package=gridExtra
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting
linear mixed-effects models using lme4.
Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
Bates, D., Maechler, M., Bolker, B., & Steven Walker. (2022).
lme4: Linear mixed-effects
models using Eigen’ and S4. https://CRAN.R-project.org/package=lme4
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.
https://royalsocietypublishing.org/doi/pdf/10.1098/rstl.1763.0053
BibTeX. (2020). http://www.bibtex.org/
Bliss, C. I. (1934). The method of probits. Science. https://doi.org/10.1126/science.79.2037.38
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. https://doi.org/10.1037/xge0000558
Braumoeller, B. F. (2004). Hypothesis testing and multiplicative
interaction terms. International Organization, 58(4),
807–820. https://doi.org/10.1017/S0020818304040251
Bryan, J., the STAT 545 TAs, & Hester, J. (2020). Happy
Git and GitHub for the useR. https://happygitwithr.com
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. (2020). Bayesian item response modeling in
R with brms and Stan. http://arxiv.org/abs/1905.09501
Bürkner, P.-C. (2021). Parameterization of response distributions in
brms. https://CRAN.R-project.org/package=brms/vignettes/brms_families.html
Bürkner, P.-C. (2022a). brms reference
manual, Version 2.18.0. https://CRAN.R-project.org/package=brms/brms.pdf
Bürkner, P.-C. (2022b). brms:
Bayesian regression models using ’Stan’.
https://CRAN.R-project.org/package=brms
Bürkner, P.-C. (2022c). Estimating distributional models with
brms. https://CRAN.R-project.org/package=brms/vignettes/brms_distreg.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). Parameterization of response distributions
in brms. https://CRAN.R-project.org/package=brms/vignettes/brms_families.html
Bürkner, P.-C. (2022f). Define custom response distributions with
brms. https://CRAN.R-project.org/package=brms/vignettes/brms_customfamilies.html
Bürkner, P.-C. (2022g). Estimating non-linear models with brms.
https://CRAN.R-project.org/package=brms/vignettes/brms_nonlinear.html
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. https://doi.org/10.1177/2515245918823199
Campbell, H., & Gustafson, P. (2021). Re: Linde et
al.(2021)–The Bayes factor, HDI-ROPE and
frequentist equivalence testing are actually all equivalent. https://arxiv.org/abs/2104.07834
Carifio, J., & Perla, R. (2008). Resolving the 50-year debate around
using and misusing Likert scales. Medical
Education, 42(12), 1150–1152. https://doi.org/10.1111/j.1365-2923.2008.03172.x
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. https://thescipub.com/pdf/10.3844/jssp.2007.106.116.pdf
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). https://doi.org/10.18637/jss.v076.i01
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
Chandramouli, S. H., & Shiffrin, R. M. (2019). Commentary on
Gronau and Wagenmakers. Computational
Brain & Behavior, 2(1), 12–21. https://doi.org/10.1007/s42113-018-0017-1
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. https://doi.org/10.1142/9789812796707_0004
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. https://doi.org/10.1080/10618600.1999.10474802
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. https://doi.org/10.1007/s11336-013-9328-2
Cohen, J. (1988). Statistical power analysis for the behavioral
sciences (2nd Edition). Routledge. https://doi.org/10.4324/9780203771587
Cumming, G. (2012). Understanding the new statistics:
Effect sizes, confidence intervals, and meta-analysis.
Routledge. https://www.routledge.com/Understanding-The-New-Statistics-Effect-Sizes-Confidence-Intervals-and/Cumming/p/book/9780415879682
Dale, A. I. (2012). A history of inverse probability: From
Thomas Bayes to Karl Pearson. Springer Science
& Business Media. https://www.springer.com/gp/book/9780387988078
Eckhardt, R. (1987). Stan Ulam, John von
Neumann and the Monte Carlo method.
Argonne, USA. https://library.sciencemadness.org/lanl1_a/lib-www/pubs/00326867.pdf
Efron, B., & Morris, C. (1977). Stein’s paradox in statistics.
Scientific American, 236(5), 119–127. https://doi.org/10.1038/scientificamerican0577-119
Ellison., S. L. R. (2018). metRology:
Support for metrological applications. https://CRAN.R-project.org/package=metRology
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. https://doi.org/10.4135/9781446247600.n6
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. https://doi.org/10.1037/1082-989X.12.2.121
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
Firke, S. (2020). janitor:
Simple tools for examining and cleaning dirty data. https://CRAN.R-project.org/package=janitor
Fisher, R. A. (1925). Statistical methods for research workers, 11th
ed. Rev. Edinburgh. https://psycnet.apa.org/record/1925-15003-000
Gabry, J. (2022). Graphical posterior predictive checks using the
bayesplot package. https://CRAN.R-project.org/package=bayesplot/vignettes/graphical-ppcs.html
Gabry, J., & Mahr, T. (2022). bayesplot: Plotting for
Bayesian models. https://CRAN.R-project.org/package=bayesplot
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., 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. https://doi.org/10.1080/19345747.2011.618213
Grolemund, G., & Wickham, H. (2017). R for data science.
O’Reilly. https://r4ds.had.co.nz
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. https://doi.org/10.1007/s42113-018-0011-7
Gronau, Q. F., & Wagenmakers, E.-J. (2019b). Rejoinder:
More limitations of Bayesian leave-one-out
cross-validation. Computational Brain & Behavior,
2(1), 35–47. https://doi.org/10.1007/s42113-018-0022-4
Guber, L., Deborah. (1999). Getting what you pay for: The
debate over equity in public school expenditures. Journal of
Statistics Education, 7(2). https://www.semanticscholar.org/paper/Getting-What-You-Pay-For-The-Debate-Over-Equity-in-Guber/29c30e9dc77b56340faa5e6ad35e0741a5a83d49
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. https://www.guilford.com/books/Handbook-of-Research-Methods-for-Studying-Daily-Life/Mehl-Conner/9781462513055
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. https://doi.org/10.1080/10691898.1994.11910467
Henry, L., & Wickham, H. (2020). purrr: Functional programming
tools. https://CRAN.R-project.org/package=purrr
Heyns, E. (2020). Better BibTeX for zotero. https://retorque.re/zotero-better-bibtex/
Hocking, T. D. (2021). Directlabels: Direct labels for
multicolor plots [Manual]. https://CRAN.R-project.org/package=directlabels
Hokusai, K. (1820–1831). The great wave off
Kanagawa.
Holcomb, J., & Spalsbury, A. (2005). Teaching students to use
summary statistics and graphics to clean and analyze data. Journal
of Statistics Education, 13(3). https://doi.org/10.1080/10691898.2005.11910567
Hugh-Jones, D. (2020). santoku:
A versatile cutting tool. https://CRAN.R-project.org/package=santoku
Hyndman, R. J. (1996). Computing and graphing highest density regions.
The American Statistician, 50(2), 120–126. https://doi.org/10.1080/00031305.1996.10474359
Jean, J. (2009). RIFT SCULL.
Jeffreys, H. (1961). Theory of probability. Oxford University
Press. https://global.oup.com/academic/product/theory-of-probability-9780198503682?cc=us&lang=en&
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of
the American Statistical Association, 90(430), 773–795. https://www.stat.washington.edu/raftery/Research/PDF/kass1995.pdf
Kay, M. (2021). Extracting and visualizing tidy draws from brms
models. https://mjskay.github.io/tidybayes/articles/tidy-brms.html
Kay, M. (2022a). tidybayes:
Tidy data and ’geoms’ for Bayesian
models. https://CRAN.R-project.org/package=tidybayes
Kay, M. (2022b). Slab + interval stats and geoms. https://mjskay.github.io/ggdist/articles/slabinterval.html
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. https://doi.org/10.1145/2858036.2858558
Kelley, K., & Preacher, K. J. (2012). On effect size.
Psychological Methods, 17(2), 137. https://doi.org/10.1037/a0028086
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. https://doi.org/10.1525/collabra.158
Kolmogorov, A. N., & Bharucha-Reid, A. T. (1956). Foundations of
the theory of probability: Second English Edition.
Chelsea Publishing Company. https://www.york.ac.uk/depts/maths/histstat/kolmogorov_foundations.pdf
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. https://doi.org/10.1111/j.2044-8317.2012.02063.x
Kruschke, J. K. (2015). Doing Bayesian data analysis:
A tutorial with R, JAGS, and
Stan. Academic Press. https://sites.google.com/site/doingbayesiandataanalysis/
Kruschke, J. K. (2021). Bayesian analysis reporting guidelines.
Nature Human Behaviour, 5(10), 1282–1291. https://doi.org/10.1038/s41562-021-01177-7
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. https://doi.org/10.3758/s13423-016-1221-4
Kurz, A. S. (2023a). Statistical Rethinking with brms, ggplot2, and the
tidyverse (version 1.3.0). https://bookdown.org/content/3890/
Kurz, A. S. (2023b). Statistical Rethinking with brms,
ggplot2, and the tidyverse: Second
Edition (version 0.4.0). https://bookdown.org/content/4857/
Lakens, D., & Delacre, M. (2018). Equivalence testing and the
second generation p-value. https://doi.org/10.31234/osf.io/7k6ay
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. https://doi.org/10.1093/geronb/gby065
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. https://doi.org/10.1177/2515245918770963
Lawlor, J. (2020). PNWColors: Color
palettes inspired by nature in the US Pacific
Northwest [Manual]. https://CRAN.R-project.org/package=PNWColors
Lee, M. D., & Webb, M. R. (2005). Modeling individual differences in
cognition. Psychonomic Bulletin & Review, 12(4),
605–621. https://doi.org/10.3758/BF03196751
Legler, J., & Roback, P. (2019). Broadening your statistical
horizons: Generalized linear models and multilevel
models. https://bookdown.org/roback/bookdown-bysh/
Likert, R. (1932). A technique for the measurement of attitudes.
Archives of Psychology, 22 140, 55–55. https://legacy.voteview.com/pdf/Likert_1932.pdf
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. https://doi.org/10.1037/met0000402
Littlefield, T. (2020). lisa:
Color palettes from color lisa [Manual]. https://CRAN.R-project.org/package=lisa
Liu, C. C., & Aitkin, M. (2008). Bayes factors: Prior
sensitivity and model generalizability. Journal of Mathematical
Psychology, 52(6), 362–375. https://doi.org/10.1016/j.jmp.2008.03.002
Lucas, T. (2016). palettetown: Use
Pokemon inspired colour palettes [Manual]. https://CRAN.R-project.org/package=palettetown
Luce, R. D. (2008). Luce’s choice axiom. Scholarpedia,
3(12), 8077. https://doi.org/10.4249/scholarpedia.8077
Luce, R. D. (2012). Individual choice behavior: A
theoretical analysis. Courier Corporation. https://books.google.com?id=ERQsKkPiKkkC
Martone, M. E., Garcia-Castro, A., & VandenBos, G. R. (2018). Data
sharing in psychology. The American Psychologist,
73(2), 111–125. https://doi.org/10.1037/amp0000242
McElreath, R. (2015). Statistical rethinking: A
Bayesian course with examples in R and
Stan. CRC press. https://xcelab.net/rm/statistical-rethinking/
McElreath, R. (2020). Statistical rethinking: A
Bayesian course with examples in R and
Stan (Second Edition). CRC Press. https://xcelab.net/rm/statistical-rethinking/
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. https://yalebooks.yale.edu/book/9780300188226/theory-would-not-die
McWhite, C. D., & Wilke, C. O. (2021). colorblindr: Simulate colorblindness
in R figures [Manual]. https://github.com/clauswilke/colorblindr
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
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. https://doi.org/10.1063/1.1699114
Miller, J. (2009). What is the probability of replicating a
statistically significant effect? Psychonomic Bulletin &
Review, 16(4), 617–640. https://doi.org/10.3758/PBR.16.4.617
Müller, K., & Wickham, H. (2022). tibble: Simple data frames. https://CRAN.R-project.org/package=tibble
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. https://doi.org/10.1007/s10211-004-0095-z
Nelder, J. A., & Wedderburn, R. W. (1972). Generalized linear
models. Journal of the Royal Statistical Society: Series A
(General), 135(3), 370–384. https://doi.org/10.2307/2344614
Nicenboim, B., Schad, D., & Vasishth, S. (2021). An introduction
to Bayesian data analysis for cognitive science. https://vasishth.github.io/bayescogsci/book/
Norman, G. (2010). Likert scales, levels of measurement and the
“laws” of statistics. Advances in Health Sciences
Education, 15(5), 625–632. https://doi.org/10.1007/s10459-010-9222-y
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. https://doi.org/10.1080/19312450701641375
Pedersen, Thomas Lin. (n.d.). Draw polygons with
expansion/contraction and/or rounded corners — geom_shape.
Retrieved September 11, 2020, from https://ggforce.data-imaginist.com/reference/geom_shape.html
Pedersen, Thomas L. (2020a). Adding annotation and style. https://patchwork.data-imaginist.com/articles/guides/annotation.html
Pedersen, Thomas L. (2020b). Plot assembly. https://patchwork.data-imaginist.com/articles/guides/assembly.html
Pedersen, Thomas Lin. (2021). ggforce:
Accelerating ’ggplot2’
[Manual]. https://CRAN.R-project.org/package=ggforce
Pedersen, Thomas Lin. (2022). patchwork:
The composer of plots. https://CRAN.R-project.org/package=patchwork
Pedersen, Thomas Lin, & Crameri, F. (2021). scico: Colour palettes based on the
scientific colour-maps [Manual]. https://CRAN.R-project.org/package=scico
Pek, J., & Flora, D. B. (2018). Reporting effect sizes in original
psychological research: A discussion and tutorial.
Psychological Methods, 23(2), 208. https://doi.org/https://doi.apa.org/fulltext/2017-10871-001.html
Peng, R. D. (2020). R programming for data science. https://bookdown.org/rdpeng/rprogdatascience/
Piironen, J., & Vehtari, A. (2017). Sparsity information and
regularization in the horseshoe and other shrinkage priors.
Electronic Journal of Statistics, 11(2), 5018–5051. https://doi.org/10.1214/17-EJS1337SI
Plummer, M., Best, N., Cowles, K., & Vines, K. (2006).
CODA: Convergence diagnosis and output
analysis for MCMC. R News, 6(1), 7–11. https://journal.r-project.org/archive/
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]. https://CRAN.R-project.org/package=coda
R Core Team. (2022). R: A language and environment for
statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
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
Rosa, L., Rosa, E., Sarner, L., & Barrett, S. (1998). A close look
at therapeutic touch. JAMA : The Journal of the American Medical
Association, 279(13), 1005–1010. https://doi.org/10.1001/jama.279.13.1005
Rouder, J. N. (2016). The what, why, and how of born-open data.
Behavior Research Methods, 48(3), 1062–1069. https://doi.org/10.3758/s13428-015-0630-z
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. https://doi.org/10.1016/j.jmp.2012.08.001
Roy Rosenzweig Center for History and New Media. (2020).
Zotero. https://www.zotero.org/
Schiettekatte, N. M. D., Brandl, S. J., & Casey, J. M. (2022).
fishualize: Color palettes
based on fish species [Manual]. https://CRAN.R-project.org/package=fishualize
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
Skinner, B. F. (1956). A case history in scientific method. American
Psychologist, 11(5), 221–233. https://doi.org/10.1037/h0047662
Snee, R. D. (1974). Graphical display of two-way contingency tables.
The American Statistician, 28(1), 9–12. https://doi.org/10.1080/00031305.1974.10479053
Stan Development Team. (2022a). Stan reference manual,
Version 2.31. https://mc-stan.org/docs/reference-manual/index.html
Stan Development Team. (2022b). Stan user’s guide,
Version 2.31. https://mc-stan.org/docs/stan-users-guide/index.html
Stan Development Team. (2022c). Accessing the contents of a stanfit
object. https://CRAN.R-project.org/package=rstan/vignettes/stanfit-objects.html
Steidl, R. J., Hayes, J. P., & Schauber, E. (1997). Statistical
power analysis in wildlife research. The Journal of Wildlife
Management, 61(2), 270. https://doi.org/10.2307/3802582
Sun, S., Pan, W., & Wang, L. L. (2011). Rethinking observed power:
Concept, practice, and implications. Methodology,
7(3), 81–87. https://doi.org/10.1027/1614-2241/a000025
Thomas, L. (1997). Retrospective power analysis. Conservation
Biology, 11(1), 276–280. https://doi.org/10.1046/j.1523-1739.1997.96102.x
Vanpaemel, W. (2010). Prior sensitivity in theory testing:
An apologia for the Bayes factor. Journal
of Mathematical Psychology, 54(6), 491–498. https://doi.org/10.1016/j.jmp.2010.07.003
Vehtari, A., & Gabry, J. (2022a). Using the loo package
(Version >= 2.0.0). https://CRAN.R-project.org/package=loo/vignettes/loo2-example.html
Vehtari, A., & Gabry, J. (2022b, March 23). Bayesian stacking
and pseudo-BMA weights using the loo package. https://CRAN.R-project.org/package=loo/vignettes/loo2-weights.html
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. (2021). Rank-normalization, folding, and localization:
An improved for assessing convergence of MCMC
(with Discussion). Bayesian Analysis,
16(2), 667–718. https://doi.org/10.1214/20-BA1221
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. https://doi.org/10.1007/s42113-018-0020-6
Venables, W. N., & Ripley, B. D. (2002). Modern applied
statistics with S (Fourth Edition). Springer. http://www.stats.ox.ac.uk/pub/MASS4
Wagenmakers, E.-J. (2007). A practical solution to the pervasive
problems of p values. Psychonomic
Bulletin & Review, 14(5), 779–804. https://doi.org/10.3758/BF03194105
Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R.
(2010). Bayesian hypothesis testing for psychologists: A
tutorial on the Savage–Dickey method.
Cognitive Psychology, 60(3), 158–189. https://doi.org/10.1016/j.cogpsych.2009.12.001
Weber, S., & Bürkner, P.-C. (2022). Running brms models with
within-chain parallelization. https://CRAN.R-project.org/package=brms/vignettes/brms_threading.html
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. https://doi.org/10.1080/00031305.2012.695956
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. https://doi.org/10.1177/1745691611406923
Wickham, H. (2007). Reshaping data with the reshape package. Journal
of Statistical Software, 21(12), 1–20. https://doi.org/10.18637/jss.v021.i12
Wickham, H. (2016). ggplot2:
Elegant graphics for data analysis. Springer-Verlag
New York. https://ggplot2-book.org/
Wickham, H. (2019). stringr:
Simple, consistent wrappers for common string
operations. https://CRAN.R-project.org/package=stringr
Wickham, H. (2020a). cubelyr:
A data cube ’dplyr’ backend. https://CRAN.R-project.org/package=cubelyr
Wickham, H. (2020b). forcats:
Tools for working with categorical variables
(factors). https://CRAN.R-project.org/package=forcats
Wickham, H. (2020c). reshape2:
Flexibly reshape data: A reboot of the reshape
package. https://CRAN.R-project.org/package=reshape2
Wickham, H. (2020d). 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
Wickham, H., François, R., Henry, L., & Müller, K. (2020). dplyr: A grammar of data
manipulation. https://CRAN.R-project.org/package=dplyr
Wickham, H., Hester, J., & Francois, R. (2018). readr: Read rectangular text
data. https://CRAN.R-project.org/package=readr
Wilke, C. O. (2019). Fundamentals of data visualization. https://clauswilke.com/dataviz/
Wilke, C. O. (2020a). cowplot:
Streamlined plot theme and plot annotations for ggplot2 [Manual]. https://wilkelab.org/cowplot/
Wilke, C. O. (2020b). Themes. https://wilkelab.org/cowplot/articles/themes.html
Wilke, C. O. (2021). ggridges:
Ridgeline Plots in ’ggplot2’. https://CRAN.R-project.org/package=ggridges
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., 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
Xie, Y. (2022). Bookdown: Authoring books and technical
documents with R Markdown. Chapman and
Hall/CRC. https://bookdown.org/yihui/bookdown/
Xie, Y., Allaire, J. J., & Grolemund, G. (2022). 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
Zhu, M., & Lu, A. Y. (2004). The counter-intuitive non-informative
prior for the Bernoulli family. Journal of Statistics
Education, 12(2), 3. https://doi.org/10.1080/10691898.2004.11910734