ASReml 2

ASReml is a statistical package that fits linear mixed effects models using Residual Maximum Likelihood (REML) to provide a rich and flexible tool for the analysis of many data sets commonly arising in the agricultural, biological, medical and environmental sciences.

The strength of ASReml is the use of the Average Information (AI) algorithm and sparse matrix methods for fitting the linear mixed model. As a result ASReml analyses large and complex data sets more efficiently than other packages.

ASReml features:

Uniquely efficient and fast algorithms for mixed model analysis, saving you considerable time and effort.
Handles large data sets (of 100,000 or more observations/effects).
Allows for direct fitting of cubic smoothing splines (Verbyla et al, 1999), with user-specified knot points.
Facilitates multi-environment trials for the analysis of plant or crop improvement data
Analyses univariate and multivariate breeding and genetics data
Supports a wide range of variance models for spatial analysis
Encourages innovative modelling of longitudinal data
ASReml has already been successfully applied to:
   - Animal and plant breeding and agricultural experimentation
   - Environmental sciences
   - Medical research

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ASReml 2

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