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 (Gramacy, Taddy & Polson (2011); ) are facilitated by demos in the package; see demo(package="dynaTree").


Reference manual

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install.packages("dynaTree")

1.2-17 by Robert B. Gramacy, 2 years ago


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


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


Authors: Robert B. Gramacy [aut, cre] , Matt A. Taddy [aut] , Christoforos Anagnostopoulos [aut]


Documentation:   PDF Manual  


LGPL license


Depends on methods

Suggests interp, tgp, plgp, MASS


Suggested by datadriftR.


See at CRAN