2015
Ruvolo, P., Messinger, D., & Movellan, J. (). Infants Time Their Smiles to Make Their Moms Smile. PLOS ONE, 10(9). http://doi.org/10.1371/journal.pone.0136492
Lu, Z., Chow, S., Sherwood, A., & Zhu, H. (). Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data. Annals of Applied Statistics, 9(3), 1601-1620. https://doi.org/10.1214/15-AOAS846
Maher, J., Pincus, A., Ram, N., & Conroy, D. (). Daily physical activity and life satisfaction across adulthood. Developmental Psychology, 51(10), 1407-1419. http://doi.org/10.1037/dev0000037
Van Der Ark, L., Bolt, D., Wang, W., Douglas, J., & Chow, S. (). Quantitative Psychology Research: The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014. , 140. https://doi.org/10.1007/978-3-319-19977-1
Pritikin, J., Hunter, M., & Boker, S. (). Modular Open-Source Software for Item Factor Analysis. Educational and Psychological Measurement, 75(3), 458-474. https://doi.org/10.1177/0013164414554615
Ozonoff, S., Young, G., Landa, R., Brian, J., Bryson, S., Charman, T., Chawarska, K., Macari, S., Messinger, D., Stone, W., Zwaigenbaum, L., & Iosif, A. (). Diagnostic stability in young children at risk for autism spectrum disorder: A baby siblings research consortium study. Journal of Child Psychology and Psychiatry, 56(9), 988-998. http://doi.org/10.1111/jcpp.12421
Hammal, Z., Cohn, J., & Messinger, D. (). Head Movement Dynamics during Play and Perturbed Mother-Infant Interaction. IEEE Transactions on Affective Computing, 6(4), 361-370. http://doi.org/10.1109/TAFFC.2015.2422702
Vinciarelli, A., Esposito, A., André, E., Bonin, F., Chetouani, M., Cohn, J., Cristani, M., Fuhrmann, F., Gilmartin, E., Hammal, Z., Heylen, D., Kaiser, R., Koutsombogera, M., Potamianos, A., Renals, S., Riccardi, G., & Salah, A. (). Open Challenges in Modelling, Analysis and Synthesis of Human Behaviour in Human–Human and Human–Machine Interactions. Cognitive Computation, 7(4), 397-413. http://doi.org/10.1007/s12559-015-9326-z
Chow, S., Witkiewitz, K., Grasman, R., & Maisto, S. (). The cusp catastrophe model as cross-sectional and longitudinal mixture structural equation models. Psychological Methods, 20(1), 142-164. https://doi.org/10.1037/a0038962
Filevich, E., Dresler, M., Brick, T., & Kühn, S. (). Metacognitive mechanisms underlying lucid dreaming. Journal of Neuroscience, 35(3), 1082-1088. https://doi.org/10.1523/JNEUROSCI.3342-14.2015
Conroy, D., Ram, N., Pincus, A., Coffman, D., Lorek, A., Rebar, A., & Roche, M. (). Daily physical activity and alcohol use across the adult lifespan. Health Psychology, 34(6), 653-660. http://doi.org/10.1037/hea0000157
Boker, S., Brick, T., Pritikin, J., Wang, Y., von Oertzen, T., Brown, D., Lach, J., Estabrook, R., Hunter, M., Maes, H., & Neale, M. (). Maintained individual data distributed likelihood estimation (MIDDLE). Multivariate Behavioral Research, 50(6), 706-720. https://doi.org/10.1080/00273171.2015.1094387
Mårtensson, J., Eriksson, J., Brick, T., Lindgren, M., Johansson, M., Nyberg, L., & Lovdén, M. (). Proficiency and brain structure during intense language learning: Microstructures of Learning Novel methods and approaches for assessing structural and functional changes underlying knowledge acquisition in the brain. .
Van der Ark, A., Bolt, D., Wang, W., Douglas, J., & Chow, S. (). Quantitative Psychology Research. . https://doi.org/10.1007/978-3-319-07503-7
Fisher, Z., & Tipton, E. (). robumeta: An R-package for Robust Variance Estimation in Meta-analysis. arXiv preprint arXiv:1503.02220.
Koffer, R., Koffer, R., Ram, N., Brick, T., Almeida, D., & Tucker, C. (). MACHINE LEARNING AND GERONTOLOGY: BOOSTED REGRESSION TREES PREDICT AGE DIFFERENCES IN STRESSOR EXPERIENCE: GERONTOLOGIST. , 55(0), 461--462. https://doi.org/10.1093/geront/gnv195.10
Van Der Ark, L., Bolt, D., Wang, W., Douglas, J., & Chow, S. (). Preface. Springer Proceedings in Mathematics and Statistics, 140, V. https://doi.org/10.1007/978-3-319-19977-1
2014
Kühn, S., Brick, T., Müller, B., & Gallinat, J. (). Is this car looking at you? How anthropomorphism predicts fusiform face area activation when seeing cars. PLoS One, 9(12), e113885. https://doi.org/10.1371/journal.pone.0113885
Conroy, D., Ram, N., Pincus, A., & Rebar, A. (). Bursts of Self-Conscious Emotions in the Daily Lives of Emerging Adults. Self and Identity, 14(3), 290-313. http://doi.org/10.1080/15298868.2014.983963
Messinger, D., Duvivier, L., Warren, Z., Mahoor, M., Baker, J., Warlaumont, A., & Ruvolo, P. (). The Oxford Handbook of Affective Computing: Affective Computing, Emotional Development, and Autism. . http://doi.org/10.1093/oxfordhb/9780199942237.013.012
Zhang, G., Browne, M., Ong, A., & Chow, S. (). Analytic Standard Errors for Exploratory Process Factor Analysis. Psychometrika, 79(3), 444-469. https://doi.org/10.1007/s11336-013-9365-x
von Oertzen, T., & Brick, T. (). Efficient Hessian computation using sparse matrix derivatives in RAM notation. Behavior Research Methods, 46(2), 385-395. https://doi.org/10.3758/s13428-013-0384-4
Ram, N., Conroy, D., Pincus, A., Lorek, A., Rebar, A., Roche, M., Coccia, M., Morack, J., Feldman, J., & Gerstorf, D. (). Examining the Interplay of Processes Across Multiple Time-Scales: Illustration With the Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB). Research in Human Development, 11(2), 142-160. http://doi.org/10.1080/15427609.2014.906739
Hunter, M. (). Abstract: Dynamic Mixture Modeling of a Single Simulated Case. Multivariate Behavioral Research, 49(3), 286--287. https://doi.org/10.1080/00273171.2014.912890
Hutton, R., & Chow, S. (). Longitudinal Multi-Trait-State-Method Model Using Ordinal Data. Multivariate Behavioral Research, 49(3), 269-282. https://doi.org/10.1080/00273171.2014.903832