Molenaar, D., Bolsinova, M., & Vermunt, J.K. (2018). A semi-parametric within-subject mixture approach to the analyses of responses and response times. British Journal of Mathematical and Statistical Psychology, 71(2), 205-228.

Tijmstra, J., Bolsinova, M., & Jeon, M. (2018). General mixture item response models with different item response structures: Exposition with an application to Likert scales. Behavior research methods, 1-20.

Chow, S.-M., Ou, L., Ciptadi, A., Prince, E., You, D., Hunter, M. D., Rehg, J. M., Rozga, A., & Messinger, D. S. (2018). Differential equation modeling approaches to representing sudden shifts in intensive dyadic interaction data. Psychometrika. doi:

Zhang, F., Zhang, S., Lightsey, C., Harun, S., Wong, P.C. (2017) BGS: A Large-scale Graph Visualization Tool. IS&T Electronic Imaging: Visualization and Data Analysis, IS&T.

da Silva, M. A., de Oliveira, E. S., von Davier, A. A., & Bazán, J. L. (2017). Estimating the DINA model parameters using the No‐U‐Turn Sampler. Biometrical Journal.

Chopade, P., Zhan, J., (2017) A Framework for Community Detection in Large Networks using a Game-theoretic Modeling. IEEE Transactions on Big Data, 3(3), (pp. 276-288). DOI: 10.1109/TBDATA.2016.2628725.

Chopade P., Yudelson, M., Deonovic, B., von Davier, A. A. (2018), Modeling Dynamic Team Interactions for Intelligent Tutoring. In Army Research Lab (ARL) Book Series, Building Intelligent Tutoring Systems for Teams: What Matters (Accepted, will be published in Jan 2018).

Agard, C., von Davier, A.A. (2017). The Virtual World and Reality of Testing: Building Virtual Assessments. In Hong, J., Lissitz, R.W. (Eds), Technology Enhanced Innovative Assessment: Development, Modeling, and Scoring from an Interdisciplinary Perspective (pp. 1-30). Charlotte, NC: Information Age Publishing.

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