Switch all the parallelization to futures. See
vignette("Runtime")
Switch all the progress updates to progressr. Progress updates are
now also available for parallel processing and are customizable.
Process are much more RAM efficient now.
SDModels 2.0.1
Fix bug in SDTree and SDForest where an error occurred, if X had
columns with only one unique value.
SDModels 2.0.0
Removal of data.tree dependence. Trees are now saved as a
matrix.
This results in a substantial reduction of RAM usage and space
needed to save an SDTree or SDForest. It also results in increased
computational speed.
Removal of copy, fromList, and toList. Remove the copy arguments
from all pruning involving functions.
New plotting of SDTree objects.
Improved/Robust parallelization.
Remove gpu support and dependence on GPUmatrix due to it being
scheduled for archiving.
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