Efficient Comparative Phylogenetics on Large Trees
Efficient tree manipulation functions including pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distance matrices. Calculation of phylogenetic signal and mean trait depth (trait conservatism). Ancestral state reconstruction and hidden character prediction of discrete characters, using Maximum Likelihood and Maximum Parsimony methods. Simulating models of trait evolution, and generating random trees using birth-death models.

Efficient tree manipulation functions including pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distance matrices. Calculation of phylogenetic signal and mean trait depth (trait conservatism). Ancestral state reconstruction and hidden character prediction of discrete characters, using Maximum Likelihood and Maximum Parsimony methods. Simulating and fitting models of trait evolution, and generating random trees using birth-death models.

Efficient tree manipulations for large phylogenies, including pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism). Ancestral state reconstruction and hidden character prediction of discrete characters, using maximum likelihood and maximum parsimony methods. Simulating and fitting models of trait evolution, and generating random trees using birth-death models. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.

Efficient tree manipulations for large phylogenies, including pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters. Simulating and fitting models of trait evolution, and generating random trees using birth-death models. Dating trees, comparing two trees, and reading/writing trees to a file. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.