Dynamic Trees for Learning and Design
Monte Carlo Sensitivity Analysis for dynaTree Models
Updating a Dynamic Tree Model With New Data
Copy and Delete C-side Clouds in dynaTree Objects
Calculate the ALC or predictive entropy statistic at the X locations, ...
Rejuvenate particles from the dynaTree posterior
Calculate relevance statistics for input coordinates
Retire (i.e. remove) data from the a dynaTree model
Class "dynaTree"
Internal dynaTree Functions
Dynamic trees for learning and design
Fitting Dynamic Tree Models
Extract a Path of (log) Bayes Factors
Plotting Predictive Distributions of Dynamic Tree models
Prediction for Dynamic Tree Models
Calculate the proportion of variables used in tree splits, and average...
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); <doi:10.1198/jasa.2011.ap09769>) are facilitated by demos in the package; see demo(package="dynaTree").