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 82 articles )

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  1. Balakrishnan, Kirushanthini; Sooriyarachchi, M. R.: A goodness of fit test for multilevel survival data (2018)
  2. Gawarammana, M. B. M. B. K.; Sooriyarachchi, M. R.: Comparison of methods for analyzing binary repeated measures data: a simulation-based study (comparison of methods for binary repeated measures) (2017)
  3. Lee, Youngjo; Nelder, John A.; Pawitan, Yudi: Generalized linear models with random effects. Unified analysis via (h)-likelihood (2017)
  4. Agathangelou, Sofia A.; Charalambous, Charalambos Y.; Koutselini, Mary: Reconsidering the contribution of teacher knowledge to student learning: linear or curvilinear effects? (2016) MathEduc
  5. Karakolidis, Anastasios; Pitsia, Vasiliki; Emvalotis, Anastassios: Examining students’ achievement in mathematics: a multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data for Greece (2016) MathEduc
  6. Perera, A. A. P. N. M.; Sooriyarachchi, M. R.; Wickramasuriya, S. L.: A goodness of fit test for the multilevel logistic model (2016)
  7. Zhengzheng Zhang and Richard Parker and Christopher Charlton and George Leckie and William Browne: R2MLwiN: A Package to Run MLwiN from within R (2016) not zbMATH
  8. Vourli, Georgia; Touloumi, Giota: Performance of the marginal structural models under various scenarios of incomplete marker’s values: a simulation study (2015)
  9. Adam Loy; Heike Hofmann: HLMdiag: A Suite of Diagnostics for Hierarchical Linear Models in R (2014) not zbMATH
  10. Congdon, Peter: Applied Bayesian modelling (2014)
  11. Terrance Savitsky; Susan Paddock: Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R (2014) not zbMATH
  12. Zhang, Tao; Zhang, Xingyu; Ma, Yue; Zhou, Xiaohua Andrew; Liu, Yuanyuan; Feng, Zijian; Li, Xiaosong: Bayesian spatio-temporal random coefficient time series (BaST-RCTS) model of infectious disease (2014)
  13. Carpenter, James; Kenward, Michael: Multiple imputation and its applications (2013)
  14. George Leckie; Chris Charlton: runmlwin: A Program to Run the MLwiN Multilevel Modeling Software from within Stata (2013) not zbMATH
  15. Villalta, Desirée; Guenni, Lelys; Rubio-Palis, Yasmin; Ramírez Arbeláez, Raúl: Bayesian space-time modeling of malaria incidence in Sucre state, Venezuela: spatial special issue (2013)
  16. Ahmed, Wondimu; Minnaert, Alexander; Kuyper, Hans; van der Werf, Greetje: Reciprocal relationships between math self-concept and math anxiety (2012) MathEduc
  17. Lesaffre, Emmanuel; Lawson, Andrew B.: Bayesian biostatistics (2012)
  18. Neema, Isak; Böhning, Dankmar: Monitoring murder crime in Namibia using Bayesian space-time models (2012)
  19. Snijders, Tom A. B.; Bosker, Roel J.: Multilevel analysis. An introduction to basic and advanced multilevel modeling (2012)
  20. Van De Schoot, Rens; Hoijtink, Herbert; Romeijn, Jan-Willem; Brugman, Daniel: A prior predictive loss function for the evaluation of inequality constrained hypotheses (2012)

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