Phylogenetic clustering (phyloclustering) is an
evolutionary Continuous Time Markov Chain model-based approach
to identify population structure from molecular data without
assuming linkage equilibrium. It provides a convenient
implementation of phyloclustering for DNA and SNP data, capable
of clustering individuals into subpopulations and identifying
molecular sequences representative of those subpopulations. It
is designed in C for performance, interfaced with R for
visualization, and incorporates other popular open source
software, ms, seq-gen and Hap-Clustering for simulating data
and additional analyses. See the phyclust website for more
information, documentations and examples.

An extension of some of the classes in phylobase.  Tree objects
now support subnodes on branches
methods, phylobase (>= 0.6.2), ape (>= 2.5)
This package enhancements to phylobase classes.  It
provides classes and methods which help uses manipulate
branch-annotated trees and provides supports for a few other
extra features.
processNexus.R class-phylo4d_ext.R methods-phylo4d_ext.R
setAs-phyext.R phyextPlots.R ci.R zzz.R

An Extension (for Package 'SigTree') of Some of the Classes in
Package 'phylobase'
Based on (but not identical to) the no-longer-maintained package 'phyext', provides enhancements to 'phylobase' classes, specifically for use by package 'SigTree'; provides classes and methods which help users manipulate branch-annotated trees (as in 'SigTree'); also provides support for a few other extra features.

Pedro Tarroso, Guillermo Velo-Anton, Silvia Carvalho
This package provides functions for the spatial interpolation
of genetic distances between samples. The interpolation is
based on a modified kriging method that accepts a genetic
distance matrix and generates a map of probability of lineage
presence. This package also offers tools to generate a map of
potential contact zones between groups with user-defined
thresholds in the tree to account for old and recent
divergence. Additionally, it has functions for IDW
interpolation using genetic data and midpoints.