Hi all! This thread is for discussion of standards for peer review of Regression or Supervised Learning packages, one of our initial statistical package categories. We’re interested in the community’s input as to what standards should apply specifically to such packages.

Regression and Supervised Learning includes all packages providing unique and statistical mappings between input and output data. This category includes interpolation, imputation, and inference. Note that categories are non-exclusive, and individual packages will commonly fit several categories; Regression packages may also be, for instance, Bayesian or Time-Series packages.

We’d like your answers to these questions:

- What should a regression or supervised package
*do*so as to pass a peer review process? -
*How*should regression software do that? - What should be
*documented*in regression packages? - How should regression packages be tested?

Don’t worry if your contributions might apply to other categories. We will look at suggestions across topics to see which should be elevated to more general standards. Please do comment on whether you think a standard should be required or recommended for a package to pass peer-review. We’ll use these comments as input for prioritizing standards in the future.

For some useful references, see our background chapter, and Alex Hayes’ post on statistical testing. Feel free to drop other references in the thread!