The EvolQG R package is a set of tools for evolutionary quantitative genetics we developed at the Mammal Evolution Lab at IB-USP.  It can be installed from CRAN or github and provides functions for dealing with co-variance matrices in an evolutionary quantitative genetics framework.

EvolQG implements the extension method for co-variance matrices, which controls noise in matrix estimation to improve estimates of selection gradients (described in Marroig et al. 2012); and the Selection Response Decomposition method of matrix comparisons from Marroig et al. 2011.



Evolution of Covariation

In Melo & Marroig 2015 we present a computational model for the study of the evolution of covariation. Implementations of this software are available in different languages, and can be extended for different purposes. All of the simulations in the manuscript were done in C, while the python code was used for prototyping. Python code runs somewhat slower (not terribly so), but is easier to understand.

C and python version

I recently ported the code to Julia, and this version is still untested, but preliminary tests suggests it's as fast as the C code and as easy to understand as the python code. Most extensions will probably be done in Julia.

Julia version

Fitness Trade-Offs in D. discoideum

In Wolf et al. 2015 we used a Bayesian mixed modeling approach to show that what was traditionally thought of as cheating in an altruistic interaction could be explained by trade-offs of social and non-social fitness related characters. This analysis was performed in R using the MCMCglmm package.

R mixed models code