MLwiN is a statistical software package for fitting multilevel models. It uses both maximum likelihood estimation and Markov Chain Monte Carlo (MCMC) methods. MLwiN is based on an earlier package, MLn, but with a graphical user interface (as well as other additional features)[1]. MLwiN represents multilevel models using mathematical notation including Greek letters and multiple subscripts, so the user needs to be (or become) familiar with such notation.

References in zbMATH (referenced in 105 articles )

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  1. Browne, William J.; Mccleery, Robin H.; Sheldon, Ben C.; Pettifor, Richard A.: Using cross-classified multivariate mixed response models with application to life history traits in great tits (\textitParusmajor) (2007)
  2. Candel, Math J. J. M.: Empirical Bayes estimators of the random intercept in multilevel analysis: Performance of the classical, Morris and Rao version (2007)
  3. Gretchen Carrigan and Adrian Barnett and Annette Dobson and Gita Mishra: Compensating for Missing Data from Longitudinal Studies Using WinBUGS (2007) not zbMATH
  4. Harskamp, E.; Suhre, C.: Schoenfeld’s problem solving theory in a student controlled learning environment (2007) MathEduc
  5. Manda, Samuel O. M.; Leyland, Alastair: An empirical comparison of maximum likelihood and Bayesian estimation methods for multivariate disease mapping (2007)
  6. Xu, Lei; Mazumdar, Sati; Price, Julie: Covariate adjustment in partial least squares (PLS) regression for the extraction of the spatial-temporal pattern from positron emission tomography data (2007)
  7. Young, Mary L.; Preisser, John S.; Qaqish, Bahjat F.; Wolfson, Mark: Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials (2007)
  8. Browne, William J.: MCMC algorithms for constrained variance matrices (2006)
  9. Congdon, Peter: A model for non-parametric spatially varying regression effects (2006)
  10. Lee, Youngjo; Nelder, John A.: Double hierarchical generalized linear models (with discussion) (2006)
  11. Ng, Edmond S. W.; Carpenter, James R.; Goldstein, Harvey; Rasbash, Jon: Estimation in generalised linear mixed models with binary outcomes by simulated maximum likelihood (2006)
  12. Pardoe, Iain; Weidner, Robert R.: Sentencing convicted felons in the United States: a Bayesian analysis using multilevel covariates. (With discussion and rejoinder) (2006)
  13. Plewis, Ian; Vitaro, Frank; Tremblay, Richard: Modelling repeated ordinal reports from multiple informants (2006)
  14. Blance, Andrew; Tu, Yu-Kang; Gilthorpe, Mark S.: A multilevel modelling solution to mathematical coupling (2005)
  15. Browne, W. J.; Subramanian, S. V.; Jones, K.; Goldstein, H.: Variance partitioning in multilevel logistic models that exhibit overdispersion (2005)
  16. Fielding, Antony; Yang, Min: Generalized linear mixed models for ordered responses in complex multilevel structures: effects beneath the school or college in education (2005)
  17. Gelman, Andrew: Analysis of variance -- why it is more important than ever. (With discussions and rejoinder) (2005)
  18. Liu, Xuan; Wall, Melanie M.; Hodges, James S.: Generalized spatial structural equation models (2005)
  19. Ma, Xin: Early acceleration of students in mathematics: does it promote growth and stability of growth in achievement across mathematical areas? (2005) MathEduc
  20. Moerbeek, Mirjam; Maas, Cora J. M.: Optimal experimental designs for multilevel logistic models with two binary predictors (2005)