Version beta 0.6 (2017-10-09)

New features:

• Mask argument for e.g. sex-based bivariate models where some predicted values do not exists in the population (i.e. individuals are either males/trait1 or females/trait2)

Bug fixes:

• Fixed some types in the vignette
• Added dimensions checks for multivariate functions

Misc:

• Improved formatting in some parts of the code (work ongoing)
• Updated CITATION to the Genetics paper

Version beta 0.5 (2016-09-28)

Add a bunch of new features:

• New model “ordinal” for QGparams to compute heritabilities from ordinal traits (i.e. multiple threshold)
• New functions QGicc and QGmvicc to compute Intra-Class Correlation (ICC) on the observed data scale for univariate and multivariate analyses
• New vignette “How to use QGglmm” explaining the framework, some facts about GLMMs and providing detailed examples. To access it, use vignette("QGglmmHowTo").
• Adding this file: NEWS.md

Version beta 0.4 (2016-07-13)

Multivariate code is now complete, including multivariate predictions. The univariate predictions have been changed as well: it now uses the derivative of fitness (to be calculated by the user), which allows for much improved computation time!

As usual, the code has been tested but this is still a pre-release which might contain some bugs. This version, however, is the first feature-full one, so we are switching to a beta pre-release version.

Prepared for CRAN submission. This will be the first version submitted to CRAN.

Version alpha 0.3 (2015-08-07)

This version has an almost complete multivariate code (only the QGmvpred is missing). This multivariate code has been tested, but some glitches might still lie there.

Also added a new Gaussian model (i.e. LMM), especially useful for multivariate analysis + updated all the doc and metadata. Several other minor bug/typos fixes.

Version alpha 0.2 (2015-07-02)

• Fix bug with mu/predict.
• More efficient width default

Version alpha 0.1 (2015-03-26)

This is the first “alpha” development version. The package should compile and install in R and all the functions should work without any critical issue.