Multiple.bart = function ( x.train, y.train, probit = TRUE, categorical.idx = NULL, xinfo = matrix ( 0.0, 0, 0 ), numcut = 100L, usequants = FALSE, cont = FALSE, rm.const = TRUE, k = 2.0, power = 2.0, base = 0.95, split. pwbart: Predicting new observations with a previously fitted BART. An R package which uses permutation tests to obtain p-values for linear models.Standard R linear model functions have been modi ed to produce p-values obtainedfrom permutation tests instead of from normal theory.predict.wbart: Predict new observations with a fitted BART model.predict.pbart: Predict new observations with a fitted BART model.permute.vs: Permutation-based variable selection approach You can also 'sample' the same number of items in your data frame with something like this: Random Samples and Permutations ina dataframe If it is in matrix form convert into ame use the sample function from the base package indexes sample (1:nrow (df1), size1nrow (df1)) Random Samples and Permutations.pbart: Probit BART for binary responses with Normal latents.mixtwo: Generate data with correlated and mixed-type predictors.mixone: Generate data with independent and mixed-type predictors.medianInclusion.vs: Variable selection with DART.mc.wbart: BART for continuous responses with parallel computation.mc.pwbart: Predicting new observations based on a previously fitted BART.mc.permute.vs: Permutation-based variable selection approach with parallel.mc.pbart: Probit BART for binary responses with parallel computation.mc.backward.vs: Backward selection with two filters (using parallel.mc.abc.vs: Variable selection with ABC Bayesian forest (using parallel.friedman: Generate data for an example of Friedman (1991).checkerboard: Generate data for an example of Zhu, Zeng and Kosorok (2015).bartModelMatrix: Create a matrix out of a vector or data frame.BartMixVs-package: Varibale Selection Using Bayesian Additive Regression Trees.abc.vs: Variable selection with ABC Bayesian forest.
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