Changelog

Source: NEWS.md

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 1.0.132025-06-05

  • 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 1.0.102025-05-09

  • 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 1.0.72025-04-09

  • 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 1.0.42025-02-19

  • Initial CRAN submission.