New Version of caret on CRAN

A new version of caret is on CRAN.

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Some recent features/changes:

  • The license was changed to GPL >= 2 to accommodate new code from the GA package.
  • New feature selection functions gafs and safs were added, along with helper functions and objects, were added. The package HTML was updated to expand more about feature selection. I'll talk more about these functions in an upcoming blog post.
  • A reworked version of nearZerVar based on code from Michael Benesty was added the old version is now called nzv that uses less memory and can be used in parallel.
  • sbfControl now has a multivariate option where all the predictors are exposed to the scoring function at once.
  • Several regression simulation functions were added: SLC14_1, SLC14_2, LPH07_1 and LPH07_2
  • For the input data x to train, we now respect the class of the input value to accommodate other data types (such as sparse matrices).
  • A function update.rfe was added.

Recently added models:

  • From the adabag package, two new models were added: AdaBag and AdaBoost.M1.
  • Weighted subspace random forests from the wsrf package was added.
  • Additional bagged FDA and MARS models were added (model codes bagFDAGCV and bagEarthGCV) were added that use the GCV statistic to prune the model. This leads to memory reductions during training.
  • Brenton Kenkel added ordered logistic or probit regression to train using method = "polr" from MASS
  • The adaptive mixture discriminant model from the adaptDA package
  • A robust mixture discriminant model from the robustDA package was added.
  • The multi-class discriminant model using binary predictors in the binda package was added.
  • Ensembles of partial least squares models (via the enpls package) was added.
  • plsRglm was added.
  • From the kernlab package, SVM models using string kernels were added: svmBoundrangeString, svmExpoString, svmSpectrumString
  • The model code for ada had a bug fix applied and the code was adapted to use the "sub-model trick" so it should train faster.