Bayeslingam is a method and software package for learning directed acyclic graph structures from non-experimental ('uncontrolled') data. This can be used for 'causal discovery' under suitable assumptions.
Please see our publication:
P.O. Hoyer and A.Hyttinen
"Bayesian discovery of linear acyclic causal models"
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI-2009),
pp. X-Y, 2009 (to appear).
Please also see the basic LiNGAM web page.
Here we provide the full R code implementing the method and reproducing the results in the paper. The package includes code implementing the PC algorithm, generously provided by Prof. Peter Spirtes. The package also includes a basic version of the LiNGAM method translated to R.
Version 1.1 - first public release (May 29, 2009).
Patrik O. Hoyer &
Helsinki Institute for Information Technology
Department of Computer Science
University of Helsinki