Changelog • SDModels

In case of parallel processing use random number generator “L’Ecuyer-CMRG” for reproducibility

Fix extended SDAM example

Add option to plot SDForests. The plot shows the out-of-bag performance against the number of trees. This helps to evaluate whether enough trees were used.

Added feature to select some predictors not to be regularized closes option to use some covariates not regularized in SDAM #4
Fix the length of the coefficient list to the number of predictors and name the elements
change predict_individual_j to expect a numeric new data vector instead of a whole data.frame

Add the option to select some variables as predictors in SDTree and SDForest.

Fix various bugs on edge cases with just one variable or just one tree
SDForest, regPath.SDTree, regPath.SDForest, predict.SDForest, prune.SDForest, varImp.SDTree

Fix bug in estimation of SDTree when using only one covariate (did stop splitting after one split)
Add support to predict with an SDForest using multiple cores in parallel

Fix bug in SDTree.predict(), when predicting using only one covariate

Fix bug in plot of paths using plotly (remove expression Pi in case of plotly)

Initial CRAN submission.