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Software for the Analysis of Recurrent Events

NCRM Festival Presentation on 2 July 2008

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SABRE is a program for the statistical analysis of multi-process random effect response data. These responses can take the form of binary, ordinal, count and linear recurrent events. The response sequences can be of different types. Such multi-process data is common in many research areas, e.g. the analysis of work and life histories. Sabre has been used intensively on many longitudinal datasets surveys either with recurrent information collected over time or with a clustered sampling scheme. Support for parallel processors can dramatically cut run times by many orders of magnitude. Sabre may be run as a stand-alone package or as a library for the package R or a plugin for the package Stata. The current release is 6.0.1. SABRE development was funded by ESRC as a pilot demonstrator project in e-Social Science, the CQeSS project and by Lancaster University. There is a book to accompany the R version of this is software, see Berridge, D.M., Crouchley, R., (2011), Multivariate Generalized Linear Mixed Models Using R, CRC Press, Boca Raton, FL, USA.


  • Fits the mover-stayer model, conventional logistic, logistic-normal and logistic-normal with end-points models to binary data.
  • Fits the ordered probit and logit random effect response models.
  • Fits conventional log-linear, log-linear normal and log-linear normal with end-point models to count data.
  • Substantial control is available over the parameters of the algorithm for the sophisticated user.
  • Can deal with very long sequences of data.
  • Can deal with multi-process data, where each response sequence is of a different type, currently limited to the simultaneous analysis of trivariate correlated sequences.
  • Comprehensive online user manual. and online training materials.


  • Studies of voting behaviour, trade union membership, economic activity and migration.
  • Demographic surveys.
  • Studies of infertility in humans.
  • Animal husbandry.
  • Absenteeism studies.
  • Clustered sampling schemes.
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Other links: Centre for e-Science | Centre for Applied Statistics