Dynamic trees for learning and design CRAN Task View: ExperimentalDesign (http://CRAN.R-project.org/view=ExperimentalDesign) 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") Software Depends: R (>= 2.14.0), methods Suggests: akima, tgp, plgp, MASS Robert B. Gramacy <rbgramacy@chicagobooth.edu> Robert B. Gramacy <rbgramacy@chicagobooth.edu> and Matt A. Taddy <taddy@chicagobooth.edu> LGPL 2012-04-23 application/tgz http://CRAN.R-project.org/package=dynaTree