ASRemlR 4.1
Minor releases
4.1.0.189 – 10/05/2023
Build for R4.3
4.1.0.176 – 11/05/2022
Build for R4.2
 Correction to an issue where Wald conditional pvalues could be displayed for incremental pvalues
4.1.0.160 – 18/06/2021
Build using 4.1.0
4.1.0.154 – 13/05/2021
Build using 4.0.5
 Fix to problem where ainverse() could fail intermittently when running within RStudio
 Fix to add contrast base factor to set of model terms
 Fix to a problem using weights in multisection models
 Fix to using power model in dsum() where the correct sort order was not being detected NaN values are converted to NA
4.1.0.149 – 26/01/2021
 An argument added to asreml.options to display the package build date
 Fix for when mbf factor is NA
 Fix for where fitting splines could cause a memory error
 Wald tests can now be run from the asreml call
 Fix problem with coordinate form sparse inverse in a matrix object
 Fix problem where ainverse could fail using a tibble
4.1.0.143 – 16/09/2020
CentOS 7 build for R4.0

Fix to problem where the calculation of Fprobabilities can fail when there are large denominator degrees

Improvements to starting values when facv is used

Fix to license activation problem when usernames include multibyte characters

Step size added to trace within asreml object
4.1.0.130 – 31/05/2020
Build for R 4.0
4.1.0.126 – 05/04/2020
Issues fixed:
 Fix for ‘unable to load shared object’ error on R3.6 on Mac
 Improved licensing error handling
 Added the Orange wether data set
 Updated to use ASReml mz core
 Fix to error fitting multisection power models in rparam
 Added standardized deviance residuals to GLM’s
 Fix error in sed table for predictions with one row
4.1.0.110 – 12/08/2019
Issues fixed:
 The offset function was being excluded from the call to the ASReml core
 A ‘function could not be found’ error when fitting the Negative binomial function
 Some minor corrections made for specifying initial values for some multivariate models
 Documentation added for the chkPed function.
4.1.0.106 – 01/04/2019
Issues fixed:
 Added missing MBF example data sets
 Remove unused levels in the term(s) that define the residual structure for unstructured models
 Some multivariate models incorrectly return an error in Rparam where number of observations do not match the residual model
Initial release for R 3.5
 4.1.0.98 – November 2018