References
- Ahn, W., Krawitz, A., Kim, W., Busmeyer, J. R., & Brown, J. W. (2011). A model-based fMRI analysis with hierarchical Bayesian parameter estimation. Journal of Neuroscience, Psychology, and Economics, 4(2), 95–110. https://doi.org/10.1037/a0020684
- Asparouhov, T., Hamaker, E. L., Muthén, B., Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic structural equation models dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388. https://doi.org/https://doi.org/10.1080/10705511.2017.1406803
- Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 48. https://doi.org/https://doi.org/10.18637/jss.v067.i01
- Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 16(1), 5–13. https://doi.org/https://doi.org/10.1002/wps.20375
- Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., Wigman, J. T. W., & Snippe, E. (2019). What do centrality measures measure in psychological networks ? Journal of Abnormal Psychology, 128(8), 892–903. https://doi.org/https://doi.org/10.1037/abn0000446
- Bringmann, L. F., Ferrer, E., Hamaker, E. L., Borsboom, D., & Tuerlinckx, F. (2018). Modeling nonstationary emotion dynamics in dyads using a time-varying modeling nonstationary emotion dynamics in dyads using a time-varying vector-autoregressive model. Multivariate Behavioral Research, 53(3), 293–314. https://doi.org/https://doi.org/10.1080/00273171.2018.1439722
- Bringmann, L. F., Lemmens, L. H. J. M., Huibers, M. J. H., Borsboom, D., & Tuerlinckx, F. (2015). Revealing the dynamic network structure of the Beck Depression Inventory-II. Psychological Medicine, 45(4), 747–757. https://doi.org/https://doi.org/10.1017/S0033291714001809
- Bringmann, L. F., Pe, M. L., Vissers, N., Ceulemans, E., Borsboom, D., Vanpaemel, W., Tuerlinckx, F., & Kuppens, P. (2016). Assessing temporal emotion dynamics using networks. Assessment, 23(4), 425–435. https://doi.org/https://doi.org/10.1177/1073191116645909
- Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Borsboom, D., & Tuerlinckx, F. (2013). A network approach to psychopathology : New insights into clinical longitudinal data. PLoS One, 8(4), e60188. https://doi.org/https://doi.org/10.1371/journal.pone.0060188
- Brodersen, K. H., Deserno, L., Schlagenhauf, F., Lin, Z., Penny, W. D., Buhmann, J. M., & Stephan, K. E. (2014). NeuroImage : Clinical dissecting psychiatric spectrum disorders by generative embedding. NeuroImage Clinical, 4, 98–111. https://doi.org/https://doi.org/10.1016/j.nicl.2013.11.002
- Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016). Clustering vector autoregressive models : Capturing qualitative differences in within-person dynamics. Frontiers in Psychology, 7, 1540. https://doi.org/https://doi.org/10.3389/fpsyg.2016.01540
- Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2018). Improved insight into and prediction of network dynamics by combining VAR and dimension reduction. Multivariate Behavioral Research, 53(6), 853–875. https://doi.org/https://doi.org/10.1080/00273171.2018.1516540
- Ceulemans, E., & Kiers, H. A. L. (2006). Selecting among three-mode principal component models of different types and complexities : A numerical convex hull based method. British Journal of Mathematical and Statistical Psychology, 59(Pt 1), 133–150. https://doi.org/https://doi.org/10.1348/000711005X64817
- Commandeur, J. J. F., & Koopman, S. J. (2007). An introduction to state space time series analysis. Oxford University Press.
- D’Urso, P., Di Lallo, D., & Maharaj, E. A. (2013). Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks. Soft Computing, 17(1), 83–131. https://doi.org/https://doi.org/10.1007/s00500-012-0905-6
- Dy, J. G., & Brodley, C. E. (2004). Feature selection for unsupervised learning. Journal of Machine Learning Research, 5, 845–889.
- Ebner-Priemer, U. W., & Trull, T. J. (2009). Ecological momentary assessment of mood disorders and mood ecological momentary assessment of mood disorders and mood dysregulation. Psychological Assessment, 21(4), 463–475. https://doi.org/https://doi.org/10.1037/a0017075
- Epskamp, S., Deserno, M. K., & Bringmann, L. F. (2019). mlVAR: Multi-level vector autoregression (R package version 0.4). https://cran.r-project.org/web/packages/mlVAR/mlVAR.pdf
- Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480. https://doi.org/https://doi.org/10.1080/00273171.2018.1454823
- Ernst, A. F., Timmerman, M. E., Jeronimus, B. F., & Albers, C. J. (2019). Insight into individual differences in emotion dynamics with clustering. Assessment. https://doi.org/https://doi.org/10.1177/1073191119873714
- Feingold, A. (2009). Effect sizes for growth-modeling analysis for controlled clinical trials in the same metric as for classical analysis. Psychological Methods, 14(1), 43–53. https://doi.org/https://doi.org/10.1037/a0014699
- Fröhwirth-Schnatter, S., & Kaufmann, S. (2008). Model-based clustering of multiple time series. Journal of Business & Economic Statistics, 26(1), 78–89. https://doi.org/https://doi.org/10.1198/073500107000000106
- Greene, T., Gelkopf, M., Epskamp, S., & Fried, E. (2018). Dynamic networks of PTSD symptoms during conflict. Psychological Medicine, 48(14), 2409–2417. https://doi.org/https://doi.org/10.1017/S0033291718000351
- Iijima, Y., Takano, K., & Tanno, Y. (2018). Attentional bias and its association with anxious mood dynamics. Emotion (Washington, DC), 18(5), 725–735. https://doi.org/https://doi.org/10.1037/emo0000338
- Jahng, S., Wood, P. K., & Trull, T. J. (2008). Analysis of affective instability in ecological momentary assessment: Indices using successive difference and group comparison via multilevel modeling. Psychological Methods, 13(4), 354–375. https://doi.org/https://doi.org/10.1037/a0014173
- Jones, B. L., Nagin, D. S., & Roeder, K. (2001). A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods & Research, 29(3), 374–393. https://doi.org/https://doi.org/10.1177/0049124101029003005
- Katahira, K. (2016). How hierarchical models improve point estimates of model parameters at the individual level. Journal of Mathematical Psychology, 73, 37–58. https://doi.org/https://doi.org/10.1016/j.jmp.2016.03.007
- Klippel, A., Viechtbauer, W., Reininghaus, U., Wigman, J., van Borkulo, C., Myin-Germeys, I., & Wichers, M. (2018). The cascade of stress: A network approach to explore differential dynamics in populations varying in risk for psychosis. Schizophrenia Bulletin, 44(2), 328–337. https://doi.org/https://doi.org/10.1093/schbul/sbx037
- Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010). Emotional inertia and psychological maladjustment. Psychological Science, 21(7), 984–991. https://doi.org/https://doi.org/10.1177/0956797610372634
- Kuppens, P., Sheeber, L. B., Yap, M. B. H., Whittle, S., Simmons, J. G., & Allen, N. B. (2012). Emotional inertia prospectively predicts the onset of depressive disorder in adolescence. Emotion (Washington, DC), 12(2), 283–289. https://doi.org/https://doi.org/10.1037/a0025046
- Leisch, F. (2019). FlexMix : A general framework for finite mixture models and latent class regression in R. https://cran.r-project.org/web/packages/flexmix/vignettes/flexmix-intro.pdf
- Liao, T. W. (2005). Clustering of time series data: A survey. Pattern Recognition, 38, 1857–1874. https://doi.org/https://doi.org/10.1016/j.patcog.2005.01.025
- Lodewyckx, T., Tuerlinckx, F., Kuppens, P., Allen, N. B., & Sheeber, L. (2011). A hierarchical state space approach to affective dynamics. Journal of Mathematical Psychology, 55(1), 68–83. https://doi.org/https://doi.org/10.1016/j.jmp.2010.08.004
- Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer-Verlag.
- Manning, C. D., Raghavan, P., & Schütze, H. (2009). An introduction to information retrieval. Cambridge University Press.
- McNair, D. M., Lorr, M., & Droppleman, L. F. (1992). Revised manual for the profile of mood states. Educational and Industrial Testing Services.
- Pe, M. L., & Kuppens, P. (2012). The dynamic interplay between emotions in daily life: Augmentation, blunting, and the role of appraisal overlap. Emotion (Washington, DC), 12(6), 1320–1328. https://doi.org/https://doi.org/10.1037/a0028262
- Pe, M. L., Kircanski, K., Thompson, R. J., Bringmann, L. F., Tuerlinckx, F., Mestdagh, M., Mata, J., Jaeggi, S. M., Buschkuehl, M., Jonides, J., Kuppens, P., & Gotlib, I. H. (2015). Emotion-network density in major depressive disorder. Clinical Psychological Science, 3(2), 292–300. https://doi.org/https://doi.org/10.1177/2167702614540645
- Schuurman, N., Ferrer, E., de Boer-Sonnenschein, M., & Hamaker, E. L. (2016). How to compare cross-lagged associations in a multilevel autoregressive model. Psychological Methods, 21(2), 206–221. https://doi.org/https://doi.org/10.1037/met0000062
- Scrucca, L., Fop, M., Murphy, B. T., & Raftery, A. E. (2016). mclust 5: Clustering, classification and density estimation using gaussian finite mixture models. The R Journal, 8(1), 289–317. https://journal.r-project.org/archive/2016/RJ-2016-021/RJ-2016-021.pdf https://doi.org/https://doi.org/10.32614/RJ-2016-021
- Scrucca, L., Raftery, A. E. (2014). clustvarsel : A package implementing variable selection for model-based clustering in R. https://arxiv.org/abs/1411.0606
- van Berkel, N., Ferreira, D., & Kostakos, V. (2018). The experience sampling method on mobile devices. ACM Computing Surveys, 50(6), 1–40. https://doi.org/https://doi.org/10.1145/3123988
- Vandekerckhove, J., Matzke, D., & Wagenmakers, E. (2015). Model comparison and the principle of parsimony. In J. R. Busemeyer, Z. Wang, J. T. Townsend, & A. Eidels (Eds.), The Oxford handbook of computational and mathematical psychology (pp. 1–39). Oxford University Press. https://doi.org/https://doi.org/10.1093/oxfordhb/9780199957996.013.14
- Wichers, M., Groot, P. C., & Psychosystems, ESM Group, EWS Group. (2016). Critical slowing down as a personalized early. Psychotherapy and Psychosomatics, 85(2), 114–116. https://doi.org/https://doi.org/10.1159/000441458
- Wichers, M., Schreuder, M. J., Goekoop, R., & Groen, R. N. (2019). Can we predict the direction of sudden shifts in symptoms ? Transdiagnostic implications from a complex systems perspective on psychopathology. Psychological Medicine, 49(3), 380–387. https://doi.org/https://doi.org/10.1017/S0033291718002064
- Wigman, J. T. W., van Os, J., Borsboom, D., Wardenaar, K. J., Epskamp, S., Klippel, A., MERGE, Viechtbauer, W., Myin-Germeys, I., & Wichers, M. (2015). Exploring the underlying structure of mental disorders : cross-diagnostic differences and similarities from a network perspective using both a top-down and a bottom-up approach. Psychological Medicine, 45(11), 2375–2387. https://doi.org/https://doi.org/10.1017/S0033291715000331
- Wilderjans, T. F., Ceulemans, E., & Meers, K. (2013). CHull : A generic convex-hull-based model selection method. Behavior Research Methods, 45(1), 1–15. https://doi.org/https://doi.org/10.3758/s13428-012-0238-5
- Zheng, Y., Wiebe, R. P., Cleveland, H. H., Molenaar, P. C., & Harris, K. S. (2013). An idiographic examination of day-to-day patterns of substance use craving, negative affect and tobacco use among young adults in recovery. Multivariate Behavioral Research, 48(2), 241–266. https://doi.org/https://doi.org/10.1080/00273171.2013.763012