To Dos

• Move duplicationCheck into its own function

  • Easier to test
  • Easier to try different versions
  • No real performance impact

• Alternative makeSparseMat implementations:

  • C++
    • Armadillo sparseMat
    • Eigen?
    • Eventually: OpenMP and OpenACC (GPU) support
  • R
    • dplyr
    • data.table

• Alternative duplicationCheck implementations

  • C++
    • Armadillo sparseMat
    • Eigen?

• Early stopping on interactions

  • So we can avoid the super high interactions

• Interaction restarting

  • Resume adding interactions after initial partial fit to see if it improves performance

• More extensive performance profiling (RAM and CPU)

• Clear written description of all parts of the algorithm

  • Solid examples in "On Adaptive Propensity Score Truncation in Causal Inference" (Cheng Ju et al.)

• Alternative lasso implementations

  • h2o
  • C++
    • MLPACK
  • RcppMLPACK2
    • CK: this version doesn't include the MLPACK libraries, and therefore has a much higher installation burden, esp. for windows users. Perhaps there is a pre-built bundled version somewhere.
    • CK: Also "Loading the package will crash an RStudio binary that is older than the daily build version 1.1.129."
    • NH: the project itself seems like it's an early stage kind of thing (based on a quick glance) and they don't support Windows themselves, other than recommending building from source. if RcppMLPACK is for some reason more robust (other than including the actual mlpack source) than I can see a good argument for using that; otherwise, there's not much sense in using a significantly older mlpack version just to cater to some users. Aside: if we're going to comment here, can we move this to an issue -- it's really annoying to cut my comment, reload the page, and paste it, just due to the page being edited.

• Alternative prediction implementations

  • R
    • dplyr
    • data.table
  • C++
    • Armadillo / MLPACK

• Larger algorithm re-implementation

  • Save indicator functions in a list with two vectors: variables used (e.g. x1, x3) and cutoffs (1.5, 10.2)

• Wider R ML framework support

  • mlr wrapper
  • caret wrapper

• Python implementation based on C++ core (ala xgboost, arborist, etc.)

  • then scikit-learn wrapper