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 103 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. Gottard, Anna; Calzolari, Giorgio: Estimating multiple-membership logit models with mixed effects: indirect inference versus data cloning (2017)
  4. Inan, G.; Yucel, R.: Joint GEEs for multivariate correlated data with incomplete binary outcomes (2017)
  5. Lee, Youngjo; Nelder, John A.; Pawitan, Yudi: Generalized linear models with random effects. Unified analysis via (h)-likelihood (2017)
  6. Yucel, Recai: Impact of the non-distinctness and non-ignorability on the inference by multiple imputation in multivariate multilevel data: a simulation assessment (2017)
  7. Agathangelou, Sofia A.; Charalambous, Charalambos Y.; Koutselini, Mary: Reconsidering the contribution of teacher knowledge to student learning: linear or curvilinear effects? (2016) MathEduc
  8. 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
  9. Perera, A. A. P. N. M.; Sooriyarachchi, M. R.; Wickramasuriya, S. L.: A goodness of fit test for the multilevel logistic model (2016)
  10. 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
  11. Charalambous, Christiana; Pan, Jianxin; Tranmer, Mark: Variable selection in joint modelling of the mean and variance for hierarchical data (2015)
  12. Fu, Chong Yau; Huang, Hsin-Yi; Ou, Yueh-Hsing: The effects of missing serial effects and/or heteroscedastic errors on mixed models using repeated growth data (2015)
  13. Karakaya, Jale; Karabulut, Erdem; Yucel, Recai M.: Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data (2015)
  14. Vourli, Georgia; Touloumi, Giota: Performance of the marginal structural models under various scenarios of incomplete marker’s values: a simulation study (2015)
  15. Adam Loy; Heike Hofmann: HLMdiag: A Suite of Diagnostics for Hierarchical Linear Models in R (2014) not zbMATH
  16. Congdon, Peter: Applied Bayesian modelling (2014)
  17. Terrance Savitsky; Susan Paddock: Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R (2014) not zbMATH
  18. Warrington, Nicole M.; Tilling, Kate; Howe, Laura D.; Paternoster, Lavinia; Pennell, Craig E.; Wu, Yan Yan; Briollais, Laurent: Robustness of the linear mixed effects model to error distribution assumptions and the consequences for genome-wide association studies (2014)
  19. 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)
  20. Carpenter, James; Kenward, Michael: Multiple imputation and its applications (2013)

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