AncesTrees was developed for assessing ancestry based on metric analysis. AncesTrees relies on a machine learning ensemble algorithm, random forest, to classify the human skull. In the ensemble learning paradigm, several models are generated and co-jointly used to arrive at the final decision. The random forest algorithm creates ensembles of decision trees classifiers, a non-linear and non-parametric classification technique. The database used in AncesTrees is composed by 23 craniometric variables from 1,734 individuals, representative of six major ancestral groups and selected from the Howells’ craniometric series.

http://lfa.uc.pt/ancestrees/

The purpose of CRANID is to assess a skull’s probable biological ancestry, in the  broad geographical sense of the ‘ethnographic present’.

The package allows you to use multivariate methods of linear discriminant analysis and nearest neighbour discriminant analysis with 29 measurements on an individual skull. It assumes that the individual skull is within the range of variation of modern Homo sapiens. The skulls will be classified after automated comparison with 74 samples that include 3,163 skulls from around the world.

Authored by Richard Wright. http://osteoware.si.edu/comment/196

A comprehensive PC based application for skeleton recording, uses the US Smithsonian recording standards.

See http://osteoware.si.edu/forum/osteoware-communityannouncements/cranid-richard-wright-0 for details