ASReml is a statistical package that fits linear mixed models using Residual Maximum Likelihood (REML). It is a joint venture between the Biometrics Program of NSW Department of Primary Industries and the Biomathematics Unit of Rothamsted Research. Statisticians in Britain and Australia have collaborated in its development.

References in zbMATH (referenced in 26 articles )

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  1. Calleja-Rodriguez, Ainhoa; Li, Zitong; Hallingbäck, Henrik R.; Sillanpää, Mikko J.; Wu, Harry X.; Abrahamsson, Sara; García-Gil, Maria Rosario: Analysis of phenotypic- and estimated breeding values (EBV) to dissect the genetic architecture of complex traits in a Scots pine three-generation pedigree design (2019)
  2. Hui, F. K. C.; Müller, Samuel; Welsh, A. H.: Testing random effects in linear mixed models: another look at the F-test (with discussion) (2019)
  3. Smith, Alison B.; Borg, Lauren M.; Gogel, Beverley J.; Cullis, Brian R.: Estimation of factor analytic mixed models for the analysis of multi-treatment multi-environment trial data (2019)
  4. Tanaka, Emi; Hui, Francis K. C.: Symbolic formulae for linear mixed models (2019)
  5. Verbyla, Arunas Petras: A note on model selection using information criteria for general linear models estimated using REML (2019)
  6. Verbyla, Arūnas P.; De Faveri, Joanne; Wilkie, John D.; Lewis, Tom: Tensor cubic smoothing splines in designed experiments requiring residual modelling (2018)
  7. de Faveri, Joanne; Verbyla, Arūnas P.; Cullis, Brian R.; Pitchford, Wayne S.; Thompson, Robin: Residual variance-covariance modelling in analysis of multivariate data from variety selection trials (2017)
  8. Masci, C.; Ieva, F.; Agasisti, T.; Paganoni, A. M.: Bivariate multilevel models for the analysis of mathematics and reading pupils’ achievements (2017)
  9. Bailey, R. A.; Brien, C. J.: Randomization-based models for multitiered experiments. I: A chain of randomizations (2016)
  10. George Leckie: runmixregls: A Program to Run the MIXREGLS Mixed-Effects Location Scale Software from within Stata (2014) not zbMATH
  11. Heckman, Nancy; Lockhart, Richard; Nielsen, Jason D.: Penalized regression, mixed effects models and appropriate modelling (2013)
  12. Hunt, Colleen H.; Smith, Alison B.; Jordan, David R.; Cullis, Brian R.: Predicting additive and non-additive genetic effects from trials where traits are affected by interplot competition (2013)
  13. B. Huang; Rohan Shah; Andrew George: dlmap: An R Package for Mixed Model QTL and Association Analysis (2012) not zbMATH
  14. Julian Taylor; Arunas Verbyla: R Package wgaim: QTL Analysis in Bi-Parental Populations Using Linear Mixed Models (2011) not zbMATH
  15. Stringer, Joanne K.; Cullis, Brian R.; Thompson, Robin: Joint modeling of spatial variability and within-row interplot competition to increase the efficiency of plant improvement (2011)
  16. Jarrod Hadfield: MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package (2010) not zbMATH
  17. Mishchenko, Kateryna; Rönnegård, Lars; Holmgren, Sverker; Mishchenko, Volodymyr: Assessing a multiple QTL search using the variance component model (2010)
  18. Brien, C. J.; Demétrio, C. G. B.: Formulating mixed models for experiments, including longitudinal experiments (2009)
  19. Stefanova, Katia T.; Smith, Alison B.; Cullis, Brian R.: Enhanced diagnostics for the spatial analysis of field trials (2009)
  20. Welham, S. J.; Thompson, R.: A note on bimodality in the log-likelihood function for penalized spline mixed models (2009)

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