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Siwei Liu, Lu Ou & Emilio Ferrer. (2021) Dynamic Mixture Modeling with dynr. Multivariate Behavioral Research 56:6, pages 941-955.
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Jingheng Cai, Zhibin Liang, Rongqian Sun, Chenyi Liang & Junhao Pan. (2019) Bayesian analysis of latent Markov models with non-ignorable missing data. Journal of Applied Statistics 46:13, pages 2299-2313.
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Xinyuan Song, Kai Kang, Ming Ouyang, Xuejun Jiang & Jingheng Cai. (2018) Bayesian Analysis of Semiparametric Hidden Markov Models With Latent Variables. Structural Equation Modeling: A Multidisciplinary Journal 25:1, pages 1-20.
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Sy-Miin Chow, Jungmin Lee, Abe D. Hofman, Han L. J. van der Maas, Dennis K. Pearl & Peter C. M. Molenaar. (2022) Control Theory Forecasts of Optimal Training Dosage to Facilitate Children’s Arithmetic Learning in a Digital Educational Application. Psychometrika 87:2, pages 559-592.
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Jungmin Lee, Sy-Miin Chow, Puiwa Lei, Kausalai Wijekumar & Peter C. M. Molenaar. (2021) Dynamics of learning: time-varying feedback effects within the intelligent tutoring system of structure strategy (ITSS). Educational Technology Research and Development 69:6, pages 2963-2984.
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Pablo F. Cáncer, Eduardo Estrada, Mar J. F. Ollero & Emilio Ferrer. (2021) Dynamical Properties and Conceptual Interpretation of Latent Change Score Models. Frontiers in Psychology 12.
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Hefei Liu & Xinyuan Song. (2021) Bayesian analysis of hidden Markov structural equation models with an unknown number of hidden states. Econometrics and Statistics 18, pages 29-43.
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Hefei Liu, Xinyuan Song, Yanlin Tang & Baoxue Zhang. (2020) Bayesian quantile nonhomogeneous hidden Markov models. Statistical Methods in Medical Research 30:1, pages 112-128.
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Peter Halpin, Lu Ou & Michelle LaMar. 2021. Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment. Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment
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Zhao-Hua Lu, Sy-Miin Chow, Nilam Ram & Pamela M. Cole. (2019) Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data. Psychometrika 84:2, pages 611-645.
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Kai Kang, Xinyuan Song, X. Joan Hu & Hongtu Zhu. (2018) Bayesian adaptive group lasso with semiparametric hidden Markov models. Statistics in Medicine 38:9, pages 1634-1650.
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Tinne Vander Elst, Guy Notelaers & Anders Skogstad. (2018) The reciprocal relationship between job insecurity and depressive symptoms: A latent transition analysis. Journal of Organizational Behavior 39:9, pages 1197-1218.
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Rogier A. Kievit, Andreas M. Brandmaier, Gabriel Ziegler, Anne-Laura van Harmelen, Susanne M.M. de Mooij, Michael Moutoussis, Ian M. Goodyer, Ed Bullmore, Peter B. Jones, Peter Fonagy, Ulman Lindenberger & Raymond J. Dolan. (2018) Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Developmental Cognitive Neuroscience 33, pages 99-117.
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Tara Madhyastha, Matthew Peverill, Natalie Koh, Connor McCabe, John Flournoy, Kate Mills, Kevin King, Jennifer Pfeifer & Katie A. McLaughlin. (2018) Current methods and limitations for longitudinal fMRI analysis across development. Developmental Cognitive Neuroscience 33, pages 118-128.
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Xinyuan Song, Yemao Xia & Hongtu Zhu. (2017) Hidden Markov Latent Variable Models with Multivariate Longitudinal Data. Biometrics 73:1, pages 313-323.
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Michael C. Neale, Michael D. Hunter, Joshua N. Pritikin, Mahsa Zahery, Timothy R. Brick, Robert M. Kirkpatrick, Ryne Estabrook, Timothy C. Bates, Hermine H. Maes & Steven M. Boker. (2015) OpenMx 2.0: Extended Structural Equation and Statistical Modeling. Psychometrika 81:2, pages 535-549.
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Sy-Miin Chow, Zhaohua Lu, Andrew Sherwood & Hongtu Zhu. (2014) Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm. Psychometrika 81:1, pages 102-134.
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