ddimmery/tidyhte: v0.2.1 Pre-release
- Bug fixes and fixes for build errors.
Add methods to configs which allow the addition of models and moderators after the initial instantiation. This is a first step towards the eventual recipe API.
If no moderators are specified in call to
estimate_QoI, then all moderators listed in the MCATE definition are used.
First version of the recipe API is available for use!
Move joint effect model config to a single unified location in
Save the names of features used in SuperLearner ensembles so that when out-of-sample predictions are requested, columns of all zeros can be imputed for features that do not exist. This imputation will simply eliminate errors when there is a factor variable with a level observed in training but not in testing. This error would never appear in the case of a continuous covariate, because it will always exist across splits, so it only eliminates errors due to one-hot-encoding factor variables.
Add an initial draft of a vignette on methodological details undergirding the package.
Adds a linear-only version of variable importance that calculates the reduction in residual MSE from including a particular moderator.
Adds a Regression ROC Curve diagnostic for regression models.
Allows access to predictions from effect regression.
- Is supplement to
- https://github.com/ddimmery/tidyhte/tree/v0.2.1 (URL)