Here is the alphabetized reference list in APA format:

References:

Bartlett, J. (2023). mlmi: Maximum likelihood multiple imputation (Version 1.1.2) [R package]. https://CRAN.R-project.org/package=mlmi

Bates, D., Maechler, 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

Bolker, B., & Robinson, D. (2022). broom.mixed: Tidying methods for mixed models (Version 0.2.9.4) [R package]. https://CRAN.R-project.org/package=broom.mixed

Dowle, M., & Srinivasan, A. (2023). data.table: Extension of data.frame (Version 1.14.8) [R package]. https://CRAN.R-project.org/package=data.table

Knowles, J. E., & Frederick, C. (2024). merTools: Tools for analyzing mixed effect regression models (Version 0.6.2) [R package]. https://CRAN.R-project.org/package=merTools

Lüdecke, D. (2018). “sjmisc: Data and variable transformation functions.” Journal of Open Source Software, 3(26), 754. https://doi.org/10.21105/joss.00754

Nowok, B., Raab, G. M., & Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. https://doi.org/10.18637/jss.v074.i11

Pedersen, T. (2024). patchwork: The composer of plots (Version 1.2.0) [R package]. https://CRAN.R-project.org/package=patchwork

Robitzsch, A. (2023). sirt: Supplementary item response theory models (Version 3.13-228) [R package]. https://CRAN.R-project.org/package=sirt

Robitzsch, A., & Grund, S. (2023). miceadds: Some additional multiple imputation functions, especially for 'mice' (Version 3.16-18) [R package]. https://CRAN.R-project.org/package=miceadds

Tierney, N., & Cook, D. (2023). Expanding tidy data principles to facilitate missing data exploration, visualization, and assessment of imputations. Journal of Statistical Software, 105(7), 1-31. https://doi.org/10.18637/jss.v105.i07

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York.

Health CASCADE is a Marie Sklodowska-Curie Innovative Training Network funded by the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement number 956501.