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Fit linear mixed models using advanced Residual Maximum Likelihood (REML) techniques

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Harness the power of REML

ASReml-SA is powerful statistical software specially designed for mixed models using Residual Maximum Likelihood (REML) to estimate the parameters. Linear mixed effects models provide a rich and flexible tool for the analysis of many data sets commonly arising in animal, plant and aqua breeding, agriculture, environmental sciences and medical sciences.
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New release for 2021

Our latest release, ASReml-SA 4.2, has three times as much available memory as 4.1 and can therefore handle much larger analyses. Plus with parallel processing and the ability to allocate memory to certain tasks, we've also made it much faster: check out the comparison table below to see speed gains we've achieved for a selection of analyses.
ASReml-SA 4.2 - processing speed gains
ASReml-SA 4.2 delivers impressive gains in processing speed. These gains depend upon a variety of factors including: microprocessor, machine power, dataset size and type of analysis run, among others, and they will vary between users. Below are some example gains the ASReml team were able to quantify
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Features of ASReml-SA

Balanced and unbalanced longitudinal data
Balanced and unbalanced longitudinal data
Repeated measures data (multivariate analysis of variance and spline type models)
Repeated measures data (multivariate analysis of variance and spline type models)
Balanced and unbalanced experimental designs
Balanced and unbalanced experimental designs
Multi-environment trials and meta analysis
Multi-environment trials and meta analysis
Univariate and multivariate animal breeding and genetics data (involving a relationship matrix for correlated effects)
Univariate and multivariate animal breeding and genetics data (involving a relationship matrix for correlated effects)
Regular or irregular spatial data
Regular or irregular spatial data

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Our analytics and consulting services are used across the globe by seed, plant, aqua and animal breeding companies, helping them develop new varieties, strains, stocks and breeds.
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