Dynamic trees for learning and design

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Description

Inference by sequential Monte Carlo for dynamic tree regression and classification models with hooks provided for sequential design and optimization, fully online learning with drift, variable selection, and sensitivity analysis of inputs. Illustrative examples from the original dynamic trees paper are facilitated by demos in the package; see demo(package="dynaTree")

Details

For a fuller overview including a complete list of functions, and demos, please use help(package="dynaTree").

Author(s)

Robert B. Gramacy rbgramacy@chicagobooth.edu,
Matt Taddy taddy@chicagobooth.edu, and
Christoforos Anagnostopoulos christoforos.anagnostopoulos06@imperial.ac.uk

References

Taddy, M.A., Gramacy, R.B., and Polson, N. (2011). “Dynamic trees for learning and design” Journal of the American Statistical Association, 106(493), pp. 109-123; arXiv:0912.1586

http://bobby.gramacy.com/r_packages/dynaTree/

See Also

plgp, tgp