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A software package for fitting multilevel models. An important feature of MLwiN is its graphical interfaces. These allow the user easily to set up, fit and manipulate models. There are windows for data manipulation, plotting, viewing the progress of iterations etc. Predictions from fitted models can be specified directly using standard statistical notation with direct links to various kinds of derived graphs, which are automatically updated as model parameters change. Likewise, posterior residual estimates and functions of them can be linked directly to graphs, for example for model diagnostics.

  • Multivariate models are simple to specify using a special input screen. Complex variance functions can be specified and the software will allow linear and non-linear modelling of variances as functions of explanatory variables with an interactive screen, which displays the resulting model in standard notation.
  • Markov Chain Monte Carlo (MCMC) Bayesian modelling is incorporated with detailed visual diagnostics. Parametric and non-parametric bootstrapping is available and an iterated bootstrap has been implemented for unbiased estimation with multilevel generalised linear models.


Stat-JR is a brand new statistical software system: it constitutes a very different data analysis experience, featuring:

  • an interface with a range of other statistical software packages, circumventing the need to learn software-specific techniques each time functionality of a new package required, but also providing tools to help teach software-specific knowledge to those wishing to learn;
  • its own in-house MCMC-based estimation engine (eStat) for complex data modelling (including multilevel models);
  • open source templates allowing users to write their own Stat-JR functions;
  • an eBook interface providing an interactive way of reporting and disseminating science, and an innovative tool for teaching statistics.

Other software

Realcom (Developing multilevel models for REAListically COMplex social science data)

This software specialisses in three areas: models with responses at several levels of a data hierarchy, multilevel structural equation models, and measurement error modelling. The models developed under the project were estimated using Markov Chain Monte Carlo (MCMC) estimation.

  • Realcom-Impute (Realcom macros with a menu interface are available for handling quite general missing data patterns. Specially written REALCOM macros with a menu interface are now available for handling quite general missing data patterns. These will deal properly with categorical as well as normal data and also with multilevel structures. The model of interest is first set up in MLwiN in the usual way and then the variables exported to REALCOM-IMPUTE and then the imputed datasets returned to MLwiN where they will be fitted and combined automatically for the specified model of interest.


Application for performing sample size/power calculations in multilevel models via simulation


An R command interface to the MLwiN multilevel modelling software package, allowing users to fit multilevel models using MLwiN from within the R environment.


A Stata command to fit multilevel models in MLwiN from within Stata.


A Stata command to fit mixed-effects location scale models in MIXREGLS from within Stata.

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