Andersen, H. (2013). When to expect violations of causal faithfulness and why it matters. Philosophy of Science, 80(5), 672-683. https://doi.org/10.1086/673937
Azzimonti, L., Corani, G., & Scutari, M. (2020). Structure learning from related data sets with a hierarchical Bayesian score. International conference on probabilistic graphical models (pp. 5-16). PMLR.
Berkson, J. (1946). Limitations of the application of fourfold table analysis to hospital data. Biometrics Bulletin, 2(3), 47-53. https://doi.org/10.2307/3002000
Bernstein, E. E., Heeren, A., & McNally, R. J. (2017). Unpacking rumination and executive control: A network perspective. Clinical Psychological Science, 5(5), 816-826. https://doi.org/10.1177/2167702617702717
Bird, J. C., Evans, R., Waite, F., Loe, B. S., & Freeman, D. (2019). Adolescent paranoia: Prevalence, structure, and causal mechanisms. Schizophrenia Bulletin, 45(5), 1134-1142. https://doi.org/10.1093/schbul/sby180
Blanken, T. F., Van Der Zweerde, T., Van Straten, A., Van Someren, E. J. W., Borsboom, D., & Lancee, J. (2019). Introducing network intervention analysis to investigate sequential, symptom-specific treatment effects: A demonstration in co-occurring insomnia and depression. Psychotherapy and Psychosomatics, 88(1), 52-54. https://doi.org/10.1159/000495045
Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5-13. https://doi.org/10.1002/wps.20375
Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91-121. https://doi.org/10.1146/annurev-clinpsy-050212 -185608
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., van Borkulo, C. D., van Bork, R., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(1), 1-18. https://doi.org/10.1038/s43586-021-00055-w
Briganti, G., & Linkowski, P. (2019). Item and domain network structures of the Resilience Scale for Adults in 675 university students. Epidemiology and Psychiatric Sciences, 29, e33. https://doi.org/10.1017/S2045796019000222
Briganti, G., Kornreich, C., & Linkowski, P. (2021). A network structure of manic symptoms. Brain and Behavior, 11, e02010. https://doi.org/10.1002/brb3.2010
Briganti, G. (2021, August 4). A tutorial on Bayesian networks. https://doi.org/10.17605/OSF.IO/FNE9M/
Briganti, G., Fried, E. I., & Linkowski, P. (2019). Network analysis of Contingencies of Self-Worth Scale in 680 university students. Psychiatry Research, 272, 252-257. https://doi.org/10.1016/j.psychres.2018.12.080
Briganti, G., Kempenaers, C., Braun, S., Fried, E. I., & Linkowski, P. (2018). Network analysis of empathy items from the interpersonal reactivity index in 1973 young adults. Psychiatry Research, 265(7), 87-92. https://doi.org/10.1016Zj.psychres.2018.03.082
Briganti, G., & Linkowski, P. (2020). Exploring network structure and central items of the Narcissistic Personality Inventory. International Journal of Methods in Psychiatric Research, 29(1), e1810. https://doi.org/10.1002/mpr.1810
Briganti, G., Scutari, M., & Linkowski, P. (2020). A machine learning approach to relationships among alexithymia components. Psychiatria Danubina, 32(Suppl. 1), 180-187.
Briganti, G., Scutari, M., & Linkowski, P. (2021). Network structures of symptoms from the Zung Depression Scale. Psychological Reports, 124(4), 1897-1911. https://doi.org/10.1177/0033294120942116
Briganti, G., Williams, D. R., Mulder, J., & Linkowski, P. (2020). Bayesian network structure and predictability of autistic traits. Psychological Reports. Advance online publication. https://doi.org/10.1177/0033294120978159
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/10.1371/journal.pone.0060188
Cartwright, N., & McMullin, E. (1984). How the laws of physics lie. American Journal of Physics, 52(5), 474-476. https://doi.org/10.1119/1.13641
Castelletti, F., Consonni, G., Della Vedova, M. L., & Peluso, S. (2018). Learning Markov equivalence classes of directed acyclic graphs: An objective Bayes approach. Bayesian Analysis, 13(4), 1235-1260. https://doi.org/10.1214/18-BA1101
Cernis, E., Evans, R., Ehlers, A., & Freeman, D. (2021). Dissociation in relation to other mental health conditions: An exploration using network analysis. Journal of Psychiatric Research, 136, 460-467. https://doi.org/10.1016/j.jpsychires.2020.08.023
Chickering, D. M. (2002a). Learning equivalence classes of Bayesian-net-work structures. Journal of Machine Learning Research, 2, 445-498.
Chickering, D. M. (2002b). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3(Nov), 507-554.
Constantinou, A. C., Liu, Y., Chobtham, K., Guo, Z., & Kitson, N. K. (2021). Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data. International Journal of Approximate Reasoning, 131, 151-188. https://doi.org/10.1016/j.ijar.2021.01.001
Cowell, R. G. (2005). Local propagation in conditional Gaussian Bayesian networks. Journal of Machine Learning Research, 6, 1517-1550.
Cui, R., Groot, P., & Heskes, T. (2016). Copula PC algorithm for causal discovery from mixed data. Joint European conference on machine learning and knowledge discovery in databases (pp. 377-392). Springer, Cham.
Curtiss, J., Fulford, D., Hofmann, S. G., & Gershon, A. (2019). Network dynamics of positive and negative affect in bipolar disorder. Journal of Affective Disorders, 249, 270-277. https://doi.org/10.1016/j.jad.2019.02.017
Cussens, J. (2012). Bayesian network learning with cutting planes. In C. Fabio and P. Avi (Eds.), Proceedings of the 27th conference on uncertainty in artificial intelligence (pp. 153-160). AUAI Press. https://eprints.whiterose.ac.uk/76731/1/p153_cus sens.pdf
Daly, R., Shen, Q., & Aitken, S. (2011). Learning Bayesian networks: Approaches and issues. The Knowledge Engineering Review, 26(2), 99-157. https://doi.org/10.1017/S0269888910000251
Davis, L. (1991). Handbook of genetic algorithms. Van Nostrand Reinhold.
Dawid, A. P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society. Series B. Methodological, 41(1), 1-15. https://doi.org/10.1111/j.2517-6161.1979.tb01052.x
Dawid, A. P. (2010). Beware of the DAG! Causality: Objectives and assessment (pp. 59-86). PMLR.
De Campos, C. P., & Ji, Q. (2011). Efficient structure learning of Bayesian networks using constraints. Journal of Machine Learning Research, 12, 663-689.
De Ron, J., Fried, E. I., & Epskamp, S. (2019). Psychological networks in clinical populations: Investigating the consequences of Berkson’s bias. Psychological Medicine, 51, 1-9.
Demidenko, E. (2007). Sample size determination for logistic regression revisited. Statistics in Medicine, 26(18), 3385-3397. https://doi.org/10.1002/sim.2771
Dupont, W. D., & Plummer, W. D., Jr. (1998). Power and sample size calculations for studies involving linear regression. Controlled Clinical Trials, 19(6), 589-601. https://doi.org/10.1016/S0197-2456(98)00037-3
Edwards, D. (2012). Introduction to graphical modelling. Springer Science & Business Media.
Elliott, H., Jones, P. J., & Schmidt, U. (2019). Central symptoms predict posttreatment outcomes and clinical impairment in anorexia nervosa: A network analysis. Clinical Psychological Science. Advance online publication. https://doi.org/10.1177/2167702619865958
Epskamp, S. (2020). Psychometric network models from time-series and panel data. Psychometrika, 85(1), 206-231. https://doi.org/10.1007/s11336 -020-09697-3
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617-634. https://doi.org/10.1037/met0000167
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195-212. https://doi.org/10.3758/s13428-017-0862-1
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software. Advance online publication. https://doi.org/10.18637/jss.v048.i04
Follmer, H. (1980). On the global Markov property. In L. Streit (Ed.), Quantum fields—algebras, processes (pp. 293-302). Springer. https://doi.org/10.1007/978-3-7091-8598-8_19
Fried, E. I., & Cramer, A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12(6), 999-1020. https://doi.org/10.1177/1745691617705892
Fried, E. I., Eidhof, M. B., Palic, S., Costantini, G., Huisman-van Dijk, H. M., Bockting, C. L. H., Engelhard, I., Armour, C., Nielsen, A. B. S., & Karstoft, K.-I. (2018). Replicability and generalizability of posttrau-matic stress disorder (PTSD) networks: A cross-cultural multisite study of PTSD symptoms in four trauma patient samples. Clinical Psychological Science, 6(3), 335-351. https://doi.org/10.1177/2167702617745092
Fried, E. I., Epskamp, S., Nesse, R. M., Tuerlinckx, F., & Borsboom, D. (2016). What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders, 189, 314-320. https://doi.org/10.1016/j.jad.2015.09.005
Friedman, N., & Koller, D. (2003). Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Machine Learning, 50(1/2), 95-125. https://doi.org/10.1023/A:1020249912095
Friedman, N., Nachman, I., & Peer, D. (2013). Learning Bayesian network structure from massive datasets: The “sparse candidate” algorithm. ArXiv. https://arxiv.org/abs/1301.6696
Geiger, D., & Heckerman, D. (2002). Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. Annals of Statistics, 30(5), 1412-1440. https://doi.org/10.1214/aos/1035844981
Geiger, D., Verma, T., & Pearl, J. (1990). d-separation: From theorems to algorithms. In M. Henrion, R. D. Shachter, L. N. Kanal and J. F. Lemmer (Eds.), Machine intelligence and pattern recognition (Vol. 10, pp. 139-148). Elsevier.
Gelman, A., & Carlin, J. (2014). Beyond power calculations: Assessing type S (sign) and type M (magnitude) errors. Perspectives on Psychological Science, 9(6), 641-651. https://doi.org/10.1177/1745691614551642
Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4), 74-94. https://doi.org/10.1287/inte.20.4.74
Goudie, R. J., & Mukherjee, S. (2016). A Gibbs sampler for learning DAGs. Journal of Machine Learning Research, 17(30), 1-39.
Greenland, S., Pearl, J., & Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10(1), 37-48. https://doi.org/10.1097/00001648-199901000-00008
Grzegorczyk, M. (2010). An introduction to Gaussian Bayesian networks. In Q. Yan (Ed.), Systems biology in drug discovery and development (pp. 121-147). Humana Press. https://doi.org/10.1007/978-1-60761-800-3_6
Grzegorczyk, M., & Husmeier, D. (2008). Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Machine Learning, 71(2-3), 265-305. https://doi.org/10.1007/s10994-008-5057-7
Haslbeck, J. M. B., & Fried, E. I. (2017). How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets. Psychological Medicine, 47(16), 2767-2776. https://doi.org/10.1017/S0033291717001258
Haslbeck, J. M. B., & Waldorp, L. J. (2016). mgm: Structure estimation for time-varying mixed graphical models in high-dimensional data. ArXiv. https://arxiv.org/abs/1510.06871v2
Heckerman, D., & Geiger, D. (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In P. Besnard & S. Hanks (Eds.), Proceedings of the eleventh conference on uncertainty in artificial intelligence (pp. 274-284). Morgan Kaufmann.
Hinze, V., Ford, T., Evans, R., Gjelsvik, B., & Crane, C. (2021). Exploring the relationship between pain and self-harm thoughts and behaviours in young people using network analysis. Psychological Medicine, 51, 1-10. https://doi.org/10.1017/S0033291721000295
Ising, E. (1925). Beitrag zur Theorie des Ferromagnetismus [Contribution to the theory of ferromagnetism]. Zeitschrift Fur Physik, 31(1), 253-258. https://doi.org/10.1007/BF02980577
Isvoranu, A.-M., & Epskamp, S. (2021). Which estimation method to choose in network Psychometrics? Deriving guidelines for applied researchers, PsyArXiv. https://doi.org/10.1037/met0000439
Jones, P. J. (2017). networktools: Assorted tools for identifying important nodes in networks (R package version 1.0.0) [Computer software]. https://CRAN.R-project.org/package=networktools
Jones, P. J., Mair, P., Riemann, B. C., Mugno, B. L., & McNally, R. J. (2018). A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder. Journal of Anxiety Disorders, 53(1), 1-8. https://doi.org/10.1016/j.janxdis.2017.09.008
Kalisch, M., & Buhlmann, P. (2007). Estimating high-dimensional directed acyclic graphs with the PC-algorithm. Journal of Machine Learning Research, 8, 613-636.
Kalisch, M., Machler, M., Colombo, D., Maathuis, M. H., & Buhlmann, P. (2012). Causal inference using graphical models with the R Package pcalg. Journal of Statistical Software, 47(11), 1-26. https://doi.org/10.18637/jss.v047.i11
Koivisto, M., & Sood, K. (2004). Exact Bayesian structure discovery in Bayesian networks. Journal of Machine Learning Research, 5, 549-573.
Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. MIT Press.
Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence. CRC Press. https://doi.org/10.1201/b10391
Kossakowski, J. J., Gordijn, M. C. M., Riese, H., & Waldorp, L. J. (2019). Applying a dynamical systems model and network theory to major depressive disorder. Frontiers in Psychology, 10, 1762. https://doi.org/10.3389/fpsyg.2019.01762
Kruis, J., & Maris, G. (2016). Three representations of the Ising model. Scientific Reports, 6, 34175. https://doi.org/10.1038/srep34175
Kuipers, J., Moffa, G., Kuipers, E., Freeman, D., & Bebbington, P. (2019). Links between psychotic and neurotic symptoms in the general population: An analysis of longitudinal British National Survey data using Directed Acyclic Graphs. Psychological Medicine, 49(3), 388-395. https://doi.org/10.1017/S0033291718000879
Kuipers, J., Suter, P., & Moffa, G. (2018a). Efficient sampling and structure learning of Bayesian networks. ArXiv. https://arxiv.org/abs/1803.07859
Kuipers, J., Suter, P., & Moffa, G. (2018b). Efficient structure learning and sampling of bayesian networks. ArXiv. http://arxiv.org/abs/1803.07859
Lauritzen, S. L. (1996). Graphicalmodels. Clarendon Press.
Li, H., Cabeli, V., Sella, N., & Isambert, H. (2019). Constraint-based causal structure learning with consistent separating sets. https://proceedings.neurips.cc/paper/2019/hash/e6872f5bbe75073f8c7 cfb93de7f6f3a-Abstract.html
Lin, S., Uhler, C., Sturmfels, B., & Buhlmann, P. (2014). Hypersurfaces and their singularities in partial correlation testing. Foundations ofComputational Mathematics, 14(5), 1079-1116. https://doi.org/10.1007/s10208-014-9205-0
Luo, X. G., Moffa, G., & Kuipers, J. (2020). Learning Bayesian networks from ordinal data. ArXiv. https://arxiv.org/abs/2010.15808
Maathuis, M., Drton, M., Lauritzen, S., & Wainwright, M. (2018). Handbook of graphical models. CRC Press. https://doi.org/10.1201/9780429463976
Madigan, D., York, J., & Allard, D. (1995). Bayesian graphical models for discrete data. International Statistical Review/Revue Internationale de Statistique, 63(2), 215-232. https://www.jstor.org/stable/1403615
Margaritis, D. (2003). Learning Bayesian network model structure from data. Carnegie-Mellon University, Pittsburgh School of Computer Science.
McNally, R. J. (2016). Can network analysis transform psychopathology? Behaviour Research and Therapy, 86, 95-104. https://doi.org/10.1016/j.brat.2016.06.006
McNally, R. J. (2021). Network analysis of psychopathology: Controversies and challenges. Annual Review of Clinical Psychology, 17, 31-53. https://doi.org/10.1146/annurev-clinpsy-081219-092850
McNally, R. J., Heeren, A., & Robinaugh, D. J. (2017). A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse. European Journal of Psychotraumatology, 8 (Suppl. 3), 1341276. https://doi.org/10.1080/20008198.2017.1341276
McNally, R. J., Mair, P., Mugno, B. L., & Riemann, B. C. (2017). Co-mor-bid obsessive-compulsive disorder and depression: A Bayesian network approach. Psychological Medicine, 47(7), 1204-1214. https://doi.org/10.1017/S0033291716003287
McNally, R. J., Robinaugh, D. J., Deckersbach, T., Sylvia, L. G., & Nierenberg, A. A. (2021). Estimating the symptom structure ofbipolar disorder: Energy dysregulation as a central symptom. Journal of Abnormal Psychology. Advance online publication. https://doi.org/10.1037/abn0000715
McNally, R. J., Robinaugh, D. J., Wu, G. W., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: A network approach to posttraumatic stress disorder. Clinical Psychological Science, 3(6), 836-849. https://doi.org/10.1177/2167702614553230
Moffa, G., Catone, G., Kuipers, J., Kuipers, E., Freeman, D., Marwaha, S., Lennox, B. R., Broome, M. R., & Bebbington, P. (2017). Using directed acyclic graphs in epidemiological research in psychosis: An analysis of the role of bullying in psychosis. Schizophrenia Bulletin, 43(6), 1273-1279. https://doi.org/10.1093/schbul/sbx013
Mooij, J. M., Magliacane, S., & Claassen, T. (2020). Joint causal inference from multiple contexts. Journal of Machine Learning Research, 21, 1-108.
Musella, F. (2013). A PC algorithm variation for ordinal variables. Computational Statistics, 28(6), 2749-2759. https://doi.org/10.1007/s00180-013 -0426-5
Neapolitan, R. E. (2004). Learning Bayesian networks. Pearson Prentice Hall.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann Publishers Inc.
Pearl, J. (2009). Causality (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511803161
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Hachette U.K.
Pearl, J., & Verma, T. (1991). A theory of inferred causation. Proceedings of the second international conference on principles of knowledge representation and reasoning (pp. 441-452). Morgan Kaufmann.
Pensar, J., Talvitie, T., Hyttinen, A., & Koivisto, M. (2020). A Bayesian approach for estimating causal effects from observational data. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5395-5402. https://doi.org/10.1609/aaai.v34i04.5988
R Core Team. (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R -project.org
Russell, S., & Norvig, P. (2002). Artificial intelligence: A modern approach. Prentice Hall.
Scanagatta, M., de Campos, C. P., Corani, G., & Zaffalon, M. (2015). Learning Bayesian networks with thousands of variables. Advances in Neural Information Processing Systems, 28, 1864-1872.
Scutari, M. (2010). Learning Bayesian networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1-22. https://doi.org/10.18637/jss.v035.i03
Scutari, M. (2020). Bayesian network models for incomplete and dynamic data.
Scutari, M., & Denis, J.-B. (2021). Bayesian networks: With examples in R (2nd ed.). CRC Press. https://doi.org/10.1201/9780429347436
Scutari, M., Graafland, C. E., & Gutierrez, J. M. (2018). Who learns better Bayesian network structures: Constraint-based, score-based or hybrid algorithms? Proceedings of the ninth international conference on probabilistic graphical models (pp. 416-427). PMLR.
Song, L., Kolar, M., & Xing, E. (2009). Time-varying dynamic Bayesian networks. Advances in Neural Information Processing Systems, 22, 1732-1740.
Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search. Springer. https://doi.org/10.1007/978-1-4612-2748-9
Spirtes, P., & Meek, C. (1995). Learning Bayesian networks with discrete variables from data. Proceedings of first international conference on knowledge discovery and data mining (pp. 294-299). Morgan Kaufmann.
Suter, P., Kuipers, J., Moffa, G., & Beerenwinkel, N. (2021). Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG. ArXiv. http://arxiv.org/abs/2105.00488
Suzuki, J. (2017). An efficient Bayesian network structure learning strategy. New Generation Computing, 35(1), 105-124. https://doi.org/10.1007/s00354-016-0007-6
Tsagris, M., Borboudakis, G., Lagani, V., & Tsamardinos, I. (2018). Constraint-based causal discovery with mixed data. International Journal of Data Science and Analytics, 6(1), 19-30. https://doi.org/10.1007/s41060 -018-0097-y
Tsamardinos, I., Aliferis, C. F., Statnikov, A. R., & Statnikov, E. (2003). Algorithms for large scale Markov blanket discovery. FLAIRS Conference, 2, 376-380.
Tsamardinos, I., Brown, L. E., & Aliferis, C. F. (2006). The max-min hillclimbing Bayesian network structure learning algorithm. Machine Learning, 65(1), 31-78. https://doi.org/10.1007/s10994-006-6889-7
van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports, 4, 5918. https://doi.org/10.1038/srep05918
Williams, D. R., & Mulder, J. (2020a). Bayesian hypothesis testing for Gaussian graphical models: Conditional independence and order constraints. Journal of Mathematical Psychology, 99, 102441. https://doi.org/10.1016/j.jmp.2020.102441
Williams, D. R., & Mulder, J. (2020b). BGGM: Bayesian Gaussian graphical models in R. Journal of Open Source Software, 5(51), 2111. https://doi.org/10.21105/joss.02111
Young, R. C., Biggs, J. T., Ziegler, V. E., & Meyer, D. A. (1978). A rating scale for mania: Reliability, validity and sensitivity. The British Journal of Psychiatry, 133(5), 429-435. https://doi.org/10.1192/bjp.133.5.429
Zung, W. W. (1965). A self-rating depression scale. Archives of General Psychiatry, 12, 63-70. https://doi.org/10.1001/archpsyc.1965.01720310 065008[Mismatch
