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SDModels 1.0.13

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

SDModels 1.0.12

• Fix extended SDAM example

SDModels 1.0.11

• 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.

SDModels 1.0.10

• 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

SDModels 1.0.9

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

SDModels 1.0.8

• 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

SDModels 1.0.7

• 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

SDModels 1.0.6

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

SDModels 1.0.5

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

SDModels 1.0.4

• Initial CRAN submission.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.