Brandmaier, A. M., Ram, N., Wagner, G. G., & Gerstorf, D. (2017). Terminal decline in well-being: The role of multi-indicator constellations of physical health and psychosocial correlates. Developmental Psychology, 53(5), 996–1012. https://doi.org/10.1037/dev0000274

Bender, A. R., Prindle, J. J., Brandmaier, A. M., & Raz, N. (2016). White matter and memory in healthy adults: Coupled changes over two years. NeuroImage, 131, 193–204. https://doi.org/10.1016/j.neuroimage.2015.10.085

Brandmaier, A. M., Prindle, J. J., McArdle, J. J., & Lindenberger, U. (2016). Theory-guided exploration with structural equation model forests. Psychological Methods, 21(4), 566–582. https://doi.org/10.1037/met0000090

Brandmaier, A. M. (2015). pdc: An R package for complexity-based clustering of time series. Journal of Statistical Software, 67(5), 1–23. https://doi.org/10.18637/jss.v067.i05

Brandmaier, A. M., von Oertzen, T., Ghisletta, P., Hertzog, C., & Lindenberger, U. (2015). LIFESPAN: A tool for the computer-aided design of longitudinal studies. Frontiers in Psychology, 6, Article 272. https://doi.org/10.3389/fpsyg.2015.00272

Freund, J., Brandmaier, A. M., Lewejohann, L., Kirste, I., Kritzler, M., Krüger, A., Sachser, N., Lindenberger, U., & Kempermann, G. (2015). Association between exploratory activity and social individuality in genetically identical mice living in the same enriched environment. Neuroscience, 309, 140–152. https://doi.org/10.1016/j.neuroscience.2015.05.027

Karch, J. D., Sander, M. C., von Oertzen, T., Brandmaier, A. M., & Werkle-Bergner, M. (2015). Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance. NeuroImage, 118, 538–552. https://doi.org/10.1016/j.neuroimage.2015.04.038

Lever, A. G., Werkle-Bergner, M., Brandmaier, A. M., Ridderinkhof, K. R., & Geurts, H. M. (2015). Atypical working memory decline across the adult lifespan in autism spectrum disorder? Journal of Abnormal Psychology, 124(4), 1014–1026. https://doi.org/10.1037/abn0000108

von Oertzen, T., Brandmaier, A. M., & Tsang, S. (2015). Structural equation modeling with Ωnyx. Structural Equation Modeling, 22(1), 148–161. https://doi.org/10.1080/10705511.2014.935842

Ghisletta, P., Bäckman, L., Bertram, L., Brandmaier, A. M., Gerstorf, D., Liu, T., & Lindenberger, U. (2014). The Val/Met polymorphism of the Brain-Derived Neurotrophic Factor (BDNF) gene predicts decline in perceptual speed in older adults. Psychology and Aging, 29(2), 384–392. https://doi.org/10.1037/a0035201

Brandmaier, A. M., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18, 71–86. https://doi.org/10.1037/a0030001

Freund, J., Brandmaier, A. M., Lewejohann, L., Kirste, I., Kritzler, M., Krüger, A., Sachser, N., Lindenberger, U., & Kempermann, G. (2013). Emergence of individuality in genetically identical mice. Science, 340(6133), 756–759. https://doi.org/10.1126/science.1235294

von Oertzen, T., & Brandmaier, A. M. (2013). Optimal study design with identical power: An application of power equivalence to latent growth curve models. Psychology and Aging, 28(2), 414–428. https://doi.org/10.1037/a0031844

Wrzus, C., Brandmaier, A. M., von Oertzen, T., Müller, V., Wagner, G. G., & Riediger, M. (2012). A new approach for assessing sleep duration and postures from ambulatory accelerometry. PLoS ONE, 7(10), Article e48089. https://doi.org/10.1371/journal.pone.0048089

Brandmaier, A. M. (2024). Machine learning for mobile sensing data. In M. R. Mehl, M. Eid, C. Wrzus, G. M. Harari, & U. W. Ebner-Priemer (Eds.), Mobile sensing in psychology: Methods and applications (pp. 409–431). Guilford Press.

Brandmaier, A. M. (2024). Big data dimensionality reduction methods. In M. R. Mehl, M. Eid, C. Wrzus, G. M. Harari, & U. W. Ebner-Priemer (Eds.), Mobile sensing in psychology: Methods and applications (pp. 456–476). Guilford Press.

Brandmaier, A. M., & Jacobucci, R. C. (2023). Machine learning approaches to structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (2nd ed., pp. 722–739). Guilford Press.

Brandmaier, A. M., Driver, C. C., & Voelkle, M. C. (2018). Recursive partitioning in continuous time analysis. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 259–282). Springer.

Brandmaier, A. M., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2014). Exploratory data mining with structural equation model trees. In J. J. McArdle & G. Ritschard (Eds.), Contemporary issues in exploratory data mining in the behavioral sciences (pp. 96–127). Routledge.

Peikert, A. (2023). Towards transparency and Open Science: A principled perspective on computational reproducibility and preregistration [Doktorarbeit, Humboldt-Universität zu Berlin]. https://doi.org/10.18452/27056