Dynamic Trees for Learning and Design

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").


Reference manual

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1.2-10 by Robert B. Gramacy, 5 years ago


Browse source code at https://github.com/cran/dynaTree

Authors: Robert B. Gramacy <[email protected]> , Matt A. Taddy <[email protected]> and Christoforos Anagnostopoulos <[email protected]>

Documentation:   PDF Manual  

Task views: Design of Experiments (DoE) & Analysis of Experimental Data

LGPL license

Depends on methods

Suggests akima, tgp, plgp, MASS

See at CRAN