Medusa

identifying evolutionary shifts in lineage diversification for unresolved trees using stepwise AIC

Using stepwise AIC to compare models of lineage diversification, MEDUSA quantifies support for multiple shifts in birth and death rates and is well suited for use with incompletely resolved phylogenies. MEDUSA is provided as open-source software in the R language and is contained within the GEIGER package.

DOCUMENTATION

TurboMEDUSA

Now available: Turbo-MEDUSA! This revamped implementation of the MEDUSA method features 1) speedier code, 2) an AIC threshold correction for larger trees, and 3) (for unix-like systems) multicore support (but only for non-GUI instantiations). The latter in particular can handle trees of several thousand tips in a reasonable amount of time.

 

fossilmedusa medusa

incorporating fossil data into inference of shifts in diversification rates using stepwise AIC

fossilMEDUSA, building upon the original MEDUSA method, supplements molecular phylogenies with past richness information from the fossil record, fitting piecewise birth-death models to better extract information on past diversification dynamics. The method is coded in R, and will be released as part of the forthcoming GEIGER 2.0 package.

 

mecca

modeling trait evolution and lineage diversification on unresolved trees by approximate Bayesian computation

Using approximate Bayesian computation, MECCA simultaneously infers rates of diversification and trait evolution from incompletely sampled phylogenies and trait data.

 

Auteur

identifying shifts in the process of trait evolution by reversible-jump Markov chain Monte Carlo sampling

DOCUMENTATION

An implementation of reversible-jump Markov chain Monte Carlo sampling, AUTEUR is used to infer minimal complexity necessary to explain the observed comparative data. Posterior distributions of branch-wise rate estimates and model complexity are the central parameters of interest for this method. The method is introduced in a recent publication. The statistical framework of AUTEUR is provided as an open-source package in the R environment. A stable release of the software can be found at CRAN; development versions of AUTEUR are maintained at gitHUB.

 

 
  This research is funded by a grant from the National Science Foundation