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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
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.