Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
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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!

What is this package?

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.

How to install this package

Using CRAN

  • Simply use install.packages("QGglmm") as for any package.

From this GitHub

  • Install the packages on which QGglmm depends: R2Cuba and mvtnorm. install.packages(c("R2Cuba","mvtnorm"))
  • Go to the release page and download the latest release.
  • In a terminal, go to the folder where the release was downloaded and enter the following line:
    R CMD INSTALL QGglmm-xx.tar.gz where xx is the version number.
  • Alternatively, you can use the graphical tools of R-GUI or RStudio to manually install the package after download. For RStudio, this can be done using "Install Packages..." in the Tools menu, choosing "Install from: Package Archive File".

Submit feedback

If you encounter any bug or usability issue, or if you have some suggestions or feature request, please use the issue tracker. Thank you!