Revised !SLOW qualifier actionRevised GLMM VC updatingAdded advice when model fails to convergeAdded some on screen guidance. Back transform predictions from GLM modelsUpdate processing of MARGINAL GLMM models (!M in model line flags MARGINAL random terms)Update Variogram graphics figurePLOT X (i.e. no Y variable specified) […]
Patches and recent notes
Echidna Build 2.10 Apr 21 2026
Back transform predictions from GLM modelsUpdate processing of MARGINAL GLMM models (!M in model line flags MARGINAL random terms)Update Variogram graphics figure
Echidna Build 2.09 Apr 16 2026
Sorry: Internally it is labelled 2.08 Apr 16 2026 Updated !BINOMIAL !MARGINAL to expect !M rather than !R as the ‘random’ flag when fitting a binomial model with some random effects terms fitted marginally. i.e. usewk16 !bin !MARG ~ mu spec prov !m fam*reprather thanwk16 […]
Echidna Build 2.08 Mar 26 2026
Added !SELECT qualifier to !MBF lineFixed stack overflow bug in E70Cinverse Fixed bug in transformations on missing data.
Echidna Build 2.07 Mar 11 2026
Implemented some cosmetic changes suggest by Mario includingrelabelling the Fitted values in the .esy file PValues ==> Fitted_Values
Echidna Build 2.05 Feb 23 2026
There are two ways to fit marker based genomic models depending onwhether there are more markers than genotypes. Fit the model in the marker space if there are more genotypes than markers.e.g. Yield ~ mu !r Markersif no genotype replication and < 50000 markers Fit […]
Echidna Build 2.04 Feb 19 2026
Attempted to fix bug in score calc for complicated factor analytic modelsAdd multi-trait incidence counts of traits summary to .res fileFixed bug when path of working folder was more than 70 characters;limit is now 120
Echidna Build 2.03 Oct 29 2025
Fixes recent bug in processing VPREDICT units
Echidna Build 2.02 Oct 24 2025
The Mac ARM 64 build is now built with the OPENBLAS library, comparable with the INTEL ONEAPI MKL library used for the other platforms. Added !DF f and !YSS s qualifiers (as in ASReml) which allow adjustment of the residual degrees of freedom (by f) […]
Echidna Build 2.01 Sep 18 2025
For weighted analyses, whether (DH)GL(M)M or not, the base assumption isthat the weights are the inverse of the true variance, and therefore do notrequire a scaling (heterogenity/dispersion) factor . In line with ASReml 4.3,a scaling factor can be requested now via the RESIDUAL statement.(It was […]
