2020
Mundie, J., Weaver, J., Oravecz, Z., & Brick, T. (). Wear-IT Smartphone App and Framework. .
Chow, S., & Chen, M. (). Nonparametric models for longitudinal data: With implementation in R. JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS, 45(3), 369-373. https://doi.org/10.3102/1076998620915291
Cole, P., Lougheed, J., Chow, S., & Ram, N. (). Development of Emotion Regulation Dynamics Across Early Childhood: a Multiple Time-Scale Approach. Affective Science, 1(1), 28-41. https://doi.org/10.1007/s42761-020-00004-y
Heshmati, S., Heshmati, S., Oravecz, Z., Brick, T., & Roeser, R. (). Assessing psychological well-being in early adulthood: Empirical evidence for the structure of daily well-being via network analysis. Applied Developmental Science, 26(2), 207-225. https://doi.org/10.1080/10888691.2020.1766356
Knapp, K., Brick, T., Bunce, S., Bunce, S., Brick, T., Deneke, E., & Cleveland, H. (). Daily associations among craving, affect, and social interactions in the lives of patients during residential opioid use disorder treatment.. Psychology of Addictive Behaviors. https://doi.org/10.1037/adb0000612
2019
Gates, K., Fisher, Z., Arizmendi, C., Henry, T., Duffy, K., & Mucha, P. (). Assessing the robustness of cluster solutions obtained from sparse count matrices. Psychological Methods, 24(6), 675-689. https://doi.org/10.1037/met0000204
Adams, E., Marini, M., Brick, T., Paul, I., Birch, L., & Savage, J. (). Ecological momentary assessment of using food to soothe during infancy in the INSIGHT trial. International Journal of Behavioral Nutrition and Physical Activity, 16(1). https://doi.org/10.1186/s12966-019-0837-y
Chow, S. (). Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models. Multivariate Behavioral Research, 54(5), 690-718. https://doi.org/10.1080/00273171.2019.1566050
Rodgers, J., Garrison, S., O’Keefe, P., Bard, D., Hunter, M., Beasley, W., & van den Oord, E. (). Responding to a 100-Year-Old Challenge from Fisher: A Biometrical Analysis of Adult Height in the NLSY Data Using Only Cousin Pairs. Behavior Genetics, 49(5), 444-454. https://doi.org/10.1007/s10519-019-09967-6
Dopp, A., Mundey, P., Silovsky, J., Hunter, M., & Slemaker, A. (). Economic value of community-based services for problematic sexual behaviors in youth: A mixed-method cost-effectiveness analysis. Child Abuse & Neglect, 105, 1-11. https://doi.org/10.1016/j.chiabu.2019.104043
Ou, L., Hunter, M., & Chow, S. (). What's for dynr: A package for linear and nonlinear dynamic modeling in R. R Journal, 11(1). https://doi.org/10.32614/rj-2019-012
Fisher, Z., Bollen, K., & Gates, K. (). A Limited Information Estimator for Dynamic Factor Models. Multivariate Behavioral Research, 54(2), 246-263. https://doi.org/10.1080/00273171.2018.1519406
Silovsky, J., Hunter, M., & Taylor, E. (). Impact of early intervention for youth with problematic sexual behaviors and their caregivers. Journal of Sexual Aggression, 25(1), 4-15. https://doi.org/10.1080/13552600.2018.1507487
Lu, Z., Chow, S., Ram, N., & Cole, P. (). Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data. Psychometrika. https://doi.org/10.1007/s11336-019-09664-7
Varangis, E., Razlighi, Q., Habeck, C., Fisher, Z., & Stern, Y. (). Between-Network Functional Connectivity is Modified by Age and Cognitive Task Domain. Journal of Cognitive Neuroscience, 31(4), 607--622.
Ji, L., Chow, S., Crosby, B., & Teti, D. (). Exploring Sleep Dynamic of Mother-Infant Dyads Using a Regime-Switching Vector Autoregressive Model. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1697863
Li, Y., Ji, L., Oravecz, Z., Hunter, M., & Chow, S. (). dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling: Proceedings of the International Conference on Computational Social Science (ICCSS 2019). , 305-314.
Schneider, J., & Brick, T. (). plsR: Partial Least Squares Analysis for Dummies. .
Li, Y., Ji, L., Oravecz, Z., Brick, T., Hunter, M., & Chow, S. (). dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling: eISSN:1307-6892. World Academy of Science, Engineering and Technology, International Science Index 149, 2019. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 13(5), 302-311.
You, D., Hunter, M., Chen, M., & Chow, S. (). A Diagnostic Procedure for Detecting Outliers in Linear State–Space Models. Multivariate Behavioral Research, 231-255. https://doi.org/10.1080/00273171.2019.1627659
Knapp, K., Cleveland, H., Brick, T., & Bunce, S. (). Exploring daily processes of addiction: Associations between social experiences and craving among opioid addicts in treatment: Annals of Behavioral Medicine. , 53, S398--S398. https://doi.org/10.1115/DETC2016-59757
McKee, K., Hunter, M., & Neale, M. (). A Method of Correcting Estimation Failure in Latent Differential Equations with Comparisons to Kalman Filtering. Manuscript Under Review. https://doi.org/10.1080/00273171.2019.1642730
Girault, J., Arizmendi, C., Fisher, Z., Urban, C., Piven, J., & Gates, K. (). Identifying Age-Related Functional Connectivity Features Across Different Levels of Spatial Resolution: An Application of Multi-Scale GIMME: Brain Initiative. .
Jenkins, A., Fredman, S., Le, Y., Sun, X., Brick, T., Skinner, O., & McHale, S. (). Prospective associations between depressive symptoms and marital satisfaction in Black couples. Journal of Family Psychology. https://doi.org/10.1037/fam0000573
Li, Y., Ji, L., Oravecz, Z., Hunter, M., & Chow, S. (). dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling. , 305-341. https://doi.org/10.5281/zenodo.3298841