Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include 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, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. 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, using Maximum Likelihood and Maximum Parsimony methods. Simulating and fitting models of trait evolution, and generating random trees using birth-death models.

parallel, naturalsort, stats, nloptr, Matrix, RSpectra

parallel, naturalsort, stats, Matrix, RSpectra

cat
0.0-4
2003/09/30
Analysis of categorical-variable datasets with missing values
Ported to R by Ted Harding and Fernando Tusell
Original by Joseph L. Schafer <jls@stat.psu.edu>.
Ted Harding and Fernando Tusell <etptupaf@bs.ehu.es>
Analysis of categorical-variable with missing values
The software may be distributed free of charge and
used by anyone if credit is given. It has been tested
fairly well, but it comes with no guarantees and the
authors assume no liability for its use or misuse.
http://www.stat.psu.edu/~jls/misoftwa.html#aut

cat
0.0-5
2004/02/02
Analysis of categorical-variable datasets with missing values
Ported to R by Ted Harding and Fernando Tusell
Original by Joseph L. Schafer <jls@stat.psu.edu>.
Ted Harding and Fernando Tusell <etptupaf@bs.ehu.es>
Analysis of categorical-variable with missing values
The software may be distributed free of charge and
used by anyone if credit is given. It has been tested
fairly well, but it comes with no guarantees and the
authors assume no liability for its use or misuse.
http://www.stat.psu.edu/~jls/misoftwa.html#aut