ASReml is a statistical package that fits linear mixed effects models using 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.
The main development is the introduction of an alternative functional method of associating variance structures with random model terms and the residual, akin to that used in ASReml-R, as an alternative to the former structural method, where the variance models were specified separately from the model terms. Using the functional specification, the variance model for random model terms and the residual error term is specified in the linear mixed model by wrapping terms with the required variance model function. The functional approach leads to a simpler, more concise and less error-prone specification of the linear mixed model, that is more automatic for specifying multi-section residual variances.
There are also many smaller changes, to improve efficiency and convenience. These include: