References

Aalen, O. O. (1988). Heterogeneity in survival analysis. Statistics in Medicine, 7(11), 1121–1137. https://doi.org/10.1002/sim.4780071105
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2020). lme4: Linear mixed-effects models using Eigen’ and S4. https://CRAN.R-project.org/package=lme4
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
Beck, N. (1999). Modelling space and time: The event history approach. In E. Scarbrough & E. Tanenbaum (Eds.), Research strategies in social science: A guide to new approaches. Oxford University Press. https://doi.org/10.1093/0198292376.001.0001
Beck, Nathaniel, Katz, J. N., & Tucker, R. (1998). Taking time seriously: Time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science, 42(4), 1260–1288. https://doi.org/10.2307/2991857
Brennan, R. L. (2001). Generalizability Theory. Springer-Verlag. https://doi.org/10.1007/978-1-4757-3456-0
Brilleman, S. (2019). Estimating survival (time-to-event) models with rstanarm. https://github.com/stan-dev/rstanarm/blob/feature/frailty-models/vignettes/surv.Rmd
Brilleman, S. L., Elci, E. M., Novik, J. B., & Wolfe, R. (2020). Bayesian survival analysis using the rstanarm R package. https://arxiv.org/abs/2002.09633
Brown, D. R., & Gary, L. E. (1985). Predictors of depressive symptoms among unemployed Black adults. Journal of Sociology and Social Welfare, 12, 736. https://scholarworks.wmich.edu/cgi/viewcontent.cgi?article=1721&amp=&context=jssw&amp=&sei-redir=1&referer=https%253A%252F%252Fscholar.google.com%252Fscholar%253Fq%253D%252522CES-D%252522%252Bunemployment%2526hl%253Den%2526as_sdt%253D0%25252C44%2526as_ylo%253D1977%2526as_yhi%253D2000#search=%22CES-D%20unemployment%22
Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101(1), 147. https://doi.org/10.1037/0033-2909.101.1.147
Bürkner, P.-C. (2020a). Bayesian item response modeling in R with brms and Stan. arXiv:1905.09501 [Stat]. http://arxiv.org/abs/1905.09501
Bürkner, P.-C. (2021a). Estimating non-linear models with brms. https://CRAN.R-project.org/package=brms/vignettes/brms_nonlinear.html
Bürkner, P.-C. (2021b). Handle missing values with brms. https://CRAN.R-project.org/package=brms/vignettes/brms_missings.html
Bürkner, P.-C. (2021c). Parameterization of response distributions in brms. https://CRAN.R-project.org/package=brms/vignettes/brms_families.html
Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28. https://doi.org/10.18637/jss.v080.i01
Bürkner, P.-C. (2018). Advanced Bayesian multilevel modeling with the R package brms. The R Journal, 10(1), 395–411. https://doi.org/10.32614/RJ-2018-017
Bürkner, P.-C. (2020b). brms: Bayesian regression models using ’Stan. https://CRAN.R-project.org/package=brms
Bürkner, P.-C. (2021d). brms reference manual, Version 2.15.0. https://CRAN.R-project.org/package=brms/brms.pdf
Bürkner, P.-C., Gabry, J., Kay, M., & Vehtari, A. (2020). posterior: Tools for working with posterior distributions. https://mc-stan.org/posterior
Capaldi, D. M., Crosby, L., & Stoolmiller, M. (1996). Predicting the timing of first sexual intercourse for at-risk adolescent males. Child Development, 67(2), 344–359. https://doi.org/10.2307/1131818
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
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
Cooney, N. L., Kadden, R. M., Litt, M. D., & Getter, H. (1991). Matching alcoholics to coping skills or interactional therapies: Two-year follow-up results. Journal of Consulting and Clinical Psychology, 59(4), 598. https://doi.org/10.1037/0022-006X.59.4.598
Cox, David R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
Cox, David Roxbee, & Oakes, D. (1984). Analysis of survival data (Vol. 21). CRC Press. https://www.routledge.com/Analysis-of-Survival-Data/Cox-Oakes/p/book/9780412244902
Cranford, J. A., Shrout, P. E., Iida, M., Rafaeli, E., Yip, T., & Bolger, N. (2006). A procedure for evaluating sensitivity to within-person change: Can mood measures in diary studies detect change reliably? Personality and Social Psychology Bulletin, 32(7), 917–929. https://doi.org/10.1177/0146167206287721
Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972). The dependability of behavioral measurements: Theory of generalizability for scores and profiles. John Wiley & Sons. https://www.amazon.com/Dependability-Behavioral-Measurements-Generalizability-Profiles/dp/0471188506
Diekmann, A., Jungbauer-Gans, M., Krassnig, H., & Lorenz, S. (1996). Social status and aggression: A field study analyzed by survival analysis. The Journal of Social Psychology, 136(6), 761–768. https://doi.org/10.1080/00224545.1996.9712252
Ellison., S. L. R. (2018). metRology: Support for metrological applications. https://CRAN.R-project.org/package=metRology
Enders, C. K. (2010). Applied missing data analysis. Guilford press. http://www.appliedmissingdata.com/
Flinn, C. J., & Heckman, J. J. (1982). New methods for analyzing individual event histories. Sociological Methodology, 13, 99–140. https://doi.org/10.2307/270719
Frank, A. R., & Keith, T. Z. (1984). Academic abilities of persons entering and remaining in special education. Exceptional Children, 51(1), 76–77. https://eric.ed.gov/?id=EJ306852
Gabry, J. (2020). loo reference manual, Version 2.4.1. https://CRAN.R-project.org/package=loo/loo.pdf
Gabry, J., & Mahr, T. (2021). bayesplot: Plotting for Bayesian models. https://CRAN.R-project.org/package=bayesplot
Gabry, J., & Modrák, M. (2020). Visual MCMC diagnostics using the bayesplot package. https://CRAN.R-project.org/package=bayesplot/vignettes/visual-mcmc-diagnostics.html
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389–402. https://doi.org/10.1111/rssa.12378
Gamse, B. C., & Conger, D. (1997). An evaluation of the Spencer post-doctoral dissertation program. Abt Associates.
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. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press. https://doi.org/10.1017/CBO9780511790942
Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press. https://doi.org/10.1017/9781139161879
Gilks, W. R., Richardson, S., & Spiegelhalter, D. (1995). Markov chain Monte Carlo in practice. Chapman and Hall/CRC. https://www.routledge.com/Markov-Chain-Monte-Carlo-in-Practice/Gilks-Richardson-Spiegelhalter/p/book/9780412055515
Ginexi, E. M., Howe, G. W., & Caplan, R. D. (2000). Depression and control beliefs in relation to reemployment: What are the directions of effect? Journal of Occupational Health Psychology, 5(3), 323–336. https://doi.org/10.1037/1076-8998.5.3.323
Graham, S. E. (1997). The exodus from mathematics: When and why? [PhD thesis]. Harvard Graduate School of Education.
Greenwood, M. (1926). The natural duration of cancer. Reports on Public Health and Medical Subjects, 33, 1–26.
Head, R., & Pike, D. (1975). A review of response surface methodology from a biometric point of view. Biometrics, 31, 803–851.
Heckman, J., & Singer, B. S. (Eds.). (1984). Longitudinal analysis of labor market data. Cambridge University Press. https://doi.org/10.1017/CCOL0521304539
Hu, X. J., & Lawless, J. F. (1996). Estimation from truncated lifetime data with supplementary information on covariates and censoring times. Biometrika, 83(4), 747–761. https://doi.org/10.1093/biomet/83.4.747
Jaeger, B. C., Edwards, L. J., Das, K., & Sen, P. K. (2017). An R2 statistic for fixed effects in the generalized linear mixed model. Journal of Applied Statistics, 44(6), 1086–1105. https://doi.org/10.1080/02664763.2016.1193725
Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. https://doi.org/10.1080/01621459.1958.10501452
Kay, M. (2020). tidybayes: Tidy data and ’geoms’ for Bayesian models. http://mjskay.github.io/tidybayes
Keiley, Margaret Kraatz, Bates, J. E., Dodge, K. A., & Pettit, G. S. (2000). A cross-domain growth analysis: Externalizing and internalizing behaviors during 8 years of childhood. Journal of Abnormal Child Psychology, 28(2), 161–179. https://doi.org/10.1023/A:1005122814723
Keiley, M. K., & Martin, N. C. (2002). Child abuse, neglect, and juvenile delinquency: How “new” statistical approaches can inform our understanding of “old” questionsA reanalysis of Widom, 1989 [Manuscript Submitted for Publication].
Kreft, I. G., & de Leeuw, J. (1998). Introducing multilevel modeling. SAGE Publications, Inc. https://doi.org/https://dx.doi.org/10.4135/9781849209366
Kreft, I. G. G., & de Leeuw, J. (1990). Comparing four different statistical packages for hierarchical linear regression: GENMOD, HLM, ML2, and VARCL. CSE Dissemination Office, UCLA Graduate School of Education, 405 Hilgard Avenue, Los Angeles, CA 90024-1521. https://files.eric.ed.gov/fulltext/ED340731.pdf
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., & 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
Kuhn, M., Jackson, S., & Cimentada, J. (2020). corrr: Correlations in R [Manual]. https://CRAN.R-project.org/package=corrr
Kurz, A. S. (2020). Statistical rethinking with brms, ggplot2, and the tidyverse (version 1.2.0). https://doi.org/10.5281/zenodo.3693202
Kurz, A. S. (2021). Statistical rethinking with brms, ggplot2, and the tidyverse: Second Edition (version 0.2.0). https://bookdown.org/content/4857/
Lambert, B. (2018). A student’s guide to Bayesian statistics. SAGE Publications, Inc. https://ben-lambert.com/a-students-guide-to-bayesian-statistics/
Lawless, J. F. (1982). Statistical models and methods for lifetime data. John Wiley & Sons.
Li, H., & Lahiri, P. (2010). An adjusted maximum likelihood method for solving small area estimation problems. Journal of Multivariate Analysis, 101(4), 882–892. https://doi.org/10.1016/j.jmva.2009.10.009
Little, R. J. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association, 90(431), 1112–1121. https://doi.org/10.1080/01621459.1995.10476615
Little, R. J. A., & Rubin, D., B. (1987). Statistical analysis with missing data. Wiley.
Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data (third, Vol. 793). John Wiley & Sons. https://www.wiley.com/en-us/Statistical+Analysis+with+Missing+Data%2C+3rd+Edition-p-9780470526798
LoPilato, A. C., Carter, N. T., & Wang, M. (2015). Updating generalizability theory in management research: Bayesian estimation of variance components. Journal of Management, 41(2), 692–717. https://doi.org/10.1177/0149206314554215
Mare, R. D. (1994). Discrete-time bivariate hazards with unobserved heterogeneity: A partially observed contingency table approach. Sociological Methodology, 341–383. https://doi.org/10.2307/270987
McElreath, R. (2020a). Statistical rethinking: A Bayesian course with examples in R and Stan (Second Edition). CRC Press. https://xcelab.net/rm/statistical-rethinking/
McElreath, R. (2015). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press. https://xcelab.net/rm/statistical-rethinking/
McElreath, R. (2020b). rethinking R package. https://xcelab.net/rm/software/
Miller, R. G. (1981). Survival analysis. John Wiley & Sons.
Morris, C., & Tang, R. (2011). Estimating random effects via adjustment for density maximization. Statistical Science, 26(2), 271–287. https://doi.org/10.1214/10-STS349
Newsom, J. T. (2015). Longitudinal structural equation modeling: A comprehensive introduction. Routledge. http://www.longitudinalsem.com/
Nezlek, J. B. (2007). A multilevel framework for understanding relationships among traits, states, situations and behaviours. European Journal of Personality, 21(6), 789–810. https://doi.org/10.1002/per.640
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114
Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics - A Primer (1st Edition). Wiley. https://www.wiley.com/en-us/Causal+Inference+in+Statistics%3A+A+Primer-p-9781119186847
Pedersen, T. L. (2019). patchwork: The composer of plots. https://CRAN.R-project.org/package=patchwork
Peng, R. D. (2019). R programming for data science. https://bookdown.org/rdpeng/rprogdatascience/
Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd International Workshop on Distributed Statistical Computing, 124, 1–10. http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Drafts/Plummer.pdf
Plummer, M. (2012). JAGS Version 3.3.0 user manual. http://www.stat.cmu.edu/~brian/463-663/week10/articles,%20manuals/jags_user_manual.pdf
R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Radloff, L. S. (1977). The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. https://doi.org/10.1177/014662167700100306
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Second Edition). SAGE Publications, Inc. https://us.sagepub.com/en-us/nam/hierarchical-linear-models/book9230
Raudenbush, S. W., & Chan, W.-S. (2016). Growth curve analysis in accelerated longitudinal designs. Journal of Research in Crime and Delinquency, 29(4), 387–411. https://doi.org/10.1177/0022427892029004001
Revelle, W. (2020). psych: Procedures for psychological, psychometric, and personality research. https://CRAN.R-project.org/package=psych
Rights, Jason D., & Cole, D. A. (2018). Effect size measures for multilevel models in clinical child and adolescent research: New R-squared methods and recommendations. Journal of Clinical Child & Adolescent Psychology, 47(6), 863–873. https://doi.org/10.1080/15374416.2018.1528550
Rights, Jason D., & Sterba, S. K. (2020). New recommendations on the use of R-squared differences in multilevel model comparisons. Multivariate Behavioral Research, 55(4), 568–599. https://doi.org/10.1080/00273171.2019.1660605
Ripley, B. (2019). MASS: Support functions and datasets for venables and ripley’s MASS. https://CRAN.R-project.org/package=MASS
Robinson, D., & Hayes, A. (2020). broom: Convert statistical analysis objects into tidy tibbles [Manual]. https://CRAN.R-project.org/package=broom
Rogosa, D., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92(3), 726–748. https://doi.org/10.1037/0033-2909.92.3.726
Rogosa, D. R., & Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50(2), 203–228. https://doi.org/10.1007/BF02294247
Rupert G. Miller, Jr. (1997). Beyond ANOVA: Basics of applied statistics. Chapman and Hall/CRC. https://www.routledge.com/Beyond-ANOVA-Basics-of-Applied-Statistics/Jr/p/book/9780412070112
Sandberg, D. E., Meyer-Bahlburg, H. F. L., & Yager, T. J. (1991). The Child Behavior Checklist nonclinical standardization samples: Should they be utilized as norms? Journal of the American Academy of Child & Adolescent Psychiatry, 30(1), 124–134. https://doi.org/10.1097/00004583-199101000-00019
Schafer, J. L. (1997). Analysis of incomplete multivariate data. CRC press. https://www.routledge.com/Analysis-of-Incomplete-Multivariate-Data/Schafer/p/book/9780412040610
Scheike, T. H., & Jensen, T. K. (1997). A discrete survival model with random effects: An application to time to pregnancy. Biometrics, 318–329. https://doi.org/10.2307/2533117
Schloerke, B., Crowley, J., Di Cook, Briatte, F., Marbach, M., Thoen, E., Elberg, A., & Larmarange, J. (2020). GGally: Extension to ’ggplot2’. https://CRAN.R-project.org/package=GGally
Shrout, P. E., & Lane, S. P. (2012). Psychometrics. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life (pp. 302–320). The Guilford Press. https://www.guilford.com/books/Handbook-of-Research-Methods-for-Studying-Daily-Life/Mehl-Conner/9781462513055
Singer, J. D. (1992). Are special educators’ career paths special? Results from a 13-year longitudinal study. Exceptional Children, 59(3), 262–279. https://doi.org/10.1177/001440299305900309
Singer, J. D., Davidson, S. M., Graham, S., & Davidson, H. S. (1998). Physician retention in community and migrant health centers: Who stays and for how long? Medical Care, 1198–1213. http://www.jstor.org/stable/3766886
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press, USA. https://oxford.universitypressscholarship.com/view/10.1093/acprof:oso/9780195152968.001.0001/acprof-9780195152968
Snijders, T. A. B., & Bosker, R. J. (1994). Modeled variance in two-level models. Sociological Methods & Research, 22(3), 342–363. https://doi.org/10.1177/0049124194022003004
Sorenson, S. B., Rutter, C. M., & Aneshensel, C. S. (1991). Depression in the community: An investigation into age of onset. Journal of Consulting and Clinical Psychology, 59(4), 541. https://doi.org/10.1037/0022-006X.59.4.541
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Linde, A. V. D. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583–639. https://doi.org/10.1111/1467-9868.00353
Stan Development Team. (2021a). Stan reference manual, Version 2.26. https://mc-stan.org/docs/2_26/reference-manual/
Stan Development Team. (2021b). Stan user’s guide, Version 2.26. https://mc-stan.org/docs/2_26/stan-users-guide/index.html
Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702–712. https://doi.org/10.1177/1745691616658637
Sueyoshi, G. T. (1995). A class of binary response models for grouped duration data. Journal of Applied Econometrics, 10(4), 411–431. https://doi.org/10.1002/jae.3950100406
Therneau, Terry M. (2021a). A package for survival analysis in R. https://CRAN.R-project.org/package=survival/vignettes/survival.pdf
Therneau, Terry M. (2021b). survival reference manual, Version 3.2-10. https://CRAN.R-project.org/package=survival/survival.pdf
Therneau, Terry M. (2021c). survival: Survival analysis [Manual]. https://github.com/therneau/survival
Therneau, Terry M., & Grambsch, P. M. (2000). Modeling survival data: Extending the Cox model. Springer. https://link.springer.com/book/10.1007/978-1-4757-3294-8
Tomarken, A., Shelton, R., Elkins, L., & Anderson, T. (1997). Sleep deprivation and anti-depressant medication: Unique effects on positive and negative affect. American Psychological Society Meeting, Washington, DC.
Turnbull, B. W. (1974). Nonparametric estimation of a survivorship function with doubly censored data. Journal of the American Statistical Association, 69(345), 169–173. https://doi.org/10.1080/01621459.1974.10480146
Turnbull, B. W. (1976). The empirical distribution function with arbitrarily grouped, censored and truncated data. Journal of the Royal Statistical Society: Series B (Methodological), 38(3), 290–295. https://doi.org/10.1111/j.2517-6161.1976.tb01597.x
van Buuren, S. (2018). Flexible imputation of missing data (Second Edition). CRC Press. https://stefvanbuuren.name/fimd/
Vaupel, J. W., Manton, K. G., & Stallard, E. (1979). The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography, 16(3), 439–454. https://doi.org/10.2307/2061224
Vaupel, J. W., & Yashin, A. I. (1985). Heterogeneity’s ruses: Some surprising effects of selection on population dynamics. The American Statistician, 39(3), 176–185. https://doi.org/10.1080/00031305.1985.10479424
Vehtari, A., & Gabry, J. (2020). Using the loo package (version \(>\)= 2.0.0). https://CRAN.R-project.org/package=loo/vignettes/loo2-example.html
Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., & Gelman, A. (2019). loo: Efficient leave-one-out cross-validation and WAIC for bayesian models. https://CRAN.R-project.org/package=loo/
Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413–1432. https://doi.org/10.1007/s11222-016-9696-4
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P.-C. (2019). Rank-normalization, folding, and localization: An improved \(\widehat{R}\) for assessing convergence of MCMC. arXiv Preprint arXiv:1903.08008. https://arxiv.org/abs/1903.08008?
Vehtari, A., Simpson, D., Gelman, A., Yao, Y., & Gabry, J. (2021). Pareto smoothed importance sampling. https://arxiv.org/abs/1507.02646
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (Fourth Edition). Springer. http://www.stats.ox.ac.uk/pub/MASS4
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11(116), 3571–3594. http://jmlr.org/papers/v11/watanabe10a.html
Wheaton, B., Roszell, P., & Hall, K. (1997). The impact of twenty childhood and adult traumatic stressors on the risk of psychiatric disorder. In I. H. Gotlib & B. Wheaton (Eds.), Stress and adversity over the life course: Trajectories and turning points (pp. 50–72). Cambridge University Press. https://doi.org/10.1017/CBO9780511527623
Wickham, H. (2019). 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
Willett, J. B. (1989). Some results on reliability for the longitudinal measurement of change: Implications for the design of studies of individual growth. Educational and Psychological Measurement, 49(3), 587–602. https://doi.org/10.1177/001316448904900309
Williams, D. R., Rouder, J., & Rast, P. (2019). Beneath the surface: Unearthing within-Person variability and mean relations with Bayesian mixed models. https://doi.org/10.31234/osf.io/gwatq
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
Zorn, C. J., & Van Winkle, S. R. (2000). A competing risks model of Supreme Court vacancies, 1789. Political Behavior, 22(2), 145–166. https://doi.org/10.1023/A:1006667601289