This got me thinking on the future of
CRAN (which I consider vital to
R, and vital in distributing our work) in the era of super-popular meta-packages. Meta-packages are convenient, but they have a profoundly negative impact on the packages they exclude.
Users currently (with some luck) discover packages like ours and then (because they trust
CRAN) feel able to try them. With popular walled gardens that becomes much less likely. It is one thing for a standard package to duplicate another package (it is actually hard to avoid, and how work legitimately competes), it is quite another for a big-brand meta-package to pre-pick winners (and losers).
All I can say is: please give
vtreat a chance and a try. It is a package for preparing messy real-world data for predictive modeling. In addition to re-coding high cardinality categorical variables (into what we call effect-codes after Cohen, or impact-codes), it deals with missing values, can be parallelized, can be run on databases, and has years of production experience baked in.
Some places to start with
Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.