Mplus

Mplus is a statistical modeling program that provides researchers with a flexible tool to analyze their data. Mplus offers researchers a wide choice of models, estimators, and algorithms in a program that has an easy-to-use interface and graphical displays of data and analysis results. Mplus allows the analysis of both cross-sectional and longitudinal data, single-level and multilevel data, data that come from different populations with either observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried out for observed variables that are continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. In addition, Mplus has extensive capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according to any of the models included in the program.


References in zbMATH (referenced in 346 articles )

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  1. Papastamoulis, Panagiotis; Ntzoufras, Ioannis: On the identifiability of Bayesian factor analytic models (2022)
  2. Yamaguchi, Kazuhiro; Templin, Jonathan: A Gibbs sampling algorithm with monotonicity constraints for diagnostic classification models (2022)
  3. Fernando Palluzzi, Mario Grassi: SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models (2021) arXiv
  4. Merkle, E. C., Fitzsimmons, E., Uanhoro, J., Goodrich, B. : Efficient Bayesian Structural Equation Modeling in Stan (2021) not zbMATH
  5. Teresi, Jeanne A.; Wang, Chun; Kleinman, Marjorie; Jones, Richard N.; Weiss, David J.: Differential item functioning analyses of the Patient-reported outcomes measurement information system (PROMIS\circledR) measures: methods, challenges, advances, and future directions (2021)
  6. Chen, Jinsong: A partially confirmatory approach to the multidimensional item response theory with the Bayesian Lasso (2020)
  7. Feng, Yi; Harring, Jeffrey R.: Book review of: J. Wang and X. Wang, Structural equation modeling. Applications using Mplus. 2nd ed. (2020)
  8. Grønneberg, Steffen; Moss, Jonas; Foldnes, Njål: Partial identification of latent correlations with binary data (2020)
  9. Jeon, Minjeong; De Boeck, Paul; Li, Xiangrui; Lu, Zhong-Lin: Trivariate theory of mind data analysis with a conditional joint modeling approach (2020)
  10. Matthias Speidel, Jörg Drechsler, Shahab Jolani: The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond (2020) not zbMATH
  11. Okan Bulut, Christopher David Desjardins: profileR: An R package for profile analysis (2020) not zbMATH
  12. Pan, Junhao; Ip, Edward Haksing; Dubé, Laurette: Multilevel heterogeneous factor analysis and application to ecological momentary assessment (2020)
  13. Po-Hsien Huang: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood (2020) not zbMATH
  14. Wang, Jichuan; Wang, Xiaoqian: Structural equation modeling. Applications using Mplus (2020)
  15. Wenchao Ma, Jimmy de la Torre: GDINA: An R Package for Cognitive Diagnosis Modeling (2020) not zbMATH
  16. Cui, Ruifei; Bucur, Ioan Gabriel; Groot, Perry; Heskes, Tom: A novel Bayesian approach for latent variable modeling from mixed data with missing values (2019)
  17. Guerrier, Stéphane; Dupuis-Lozeron, Elise; Ma, Yanyuan; Victoria-Feser, Maria-Pia: Simulation-based bias correction methods for complex models (2019)
  18. Liu, Yang; Yang, Ji Seung; Maydeu-Olivares, Alberto: Restricted recalibration of item response theory models (2019)
  19. Ma, Wenchao: A diagnostic tree model for polytomous responses with multiple strategies (2019)
  20. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv

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Further publications can be found at: http://www.statmodel.com/references.shtml