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
cut run times by many
orders of magnitude.
Sabre may be run as a
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.
Sabre home page |
Sabre manual |
Downloading & Installing Sabre |
Sabre examples |
Training materials |
Sabre mailing list |
- 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
- Demographic surveys.
- Studies of infertility in humans.
- Animal husbandry.
- Absenteeism studies.
- Clustered sampling schemes.
Centre for e-Science |
Centre for Applied Statistics