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Due to its removal from CRAN, QGglmm dropped R2Cuba as a dependency to solve multivariate integrals. It is now using the package cubature. By taking advantage of the “vectorised” version of the algorithm, the multivariate computations of QGglmm (QGmvparams, QGvcov, QGmvmean, QGmvpsi, QGmvicc, QGmvpred) are considerably faster. Most functions are 10x-50x faster, but especially QGmvicc is 100x-500x faster. A comparison between the old and new version of the example of the man page of QGmvicc showed a decreased in computation from 25 minutes to… 4 seconds!
QGglmm computes various quantitative genetics parameters on the observed data scale from latent parameters estimated using a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the phenotypic mean, phenotypic variance and additive genetic variance on the observed data scale.
More information can be found in this article and on CRAN.
install.packages("QGglmm") as for any
package.install.packages(c("R2Cuba","mvtnorm"))R CMD INSTALL QGglmm-xx.tar.gz where xx is the
version number.If you encounter any bug or usability issue, or if you have some suggestions or feature request, please use the issue tracker. Thank you!
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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