healthcareainow depends on dplyr 1.0.0 and tibble 3.0.0. You will need these versions or later. Various hidden changes were made to be compatible with these packages’ lastest breaking changes.
healthcareainow depends on recipes 0.1.4 and caret 6.0.81. You will need these versions or later. Various hidden changes were made to be compatible with these packages’ lastest breaking changes.
bag_treesto specify the number of trees. This is updated to be compatible with recipes 0.1.4.
healthcareailibrary versions now are saved to model objects.
explore. Make counterfactual predictions across the most-important features in a model to see how those features influence predicted outcomes.
plotmethod to visualize a model’s logic.
pip. Carefully specify variables and alternative values that exert causal influence on outcomes; then get recommended actions for a given patient with expected outcomes given the actions.
plotsupport for outcome- and risk-group predictions.
plotmethod to compare performance across metrics at various thresholds.
split_train_testcan keep multiple observations of an individual in the same split via the
missingnessfinds any such strings it issues a warning with code that can be used to do the replacement.
summary.missingnessmethod for wide datasets with missingness in many columns.
prep_data, trigonometric transformations make circular features out of dates and times for more informative features in less-wide data frames.
add_best_levelsworks in deployment even if none of the columns to be created are present in the deployment observations.
prep_datacan handle logical features.
outcomedoesn’t need to be re-declared in model training if it was specified in data prep.
plot.interpretto extract glmnet estimates.
variable_importancereturns random forest or xgboost importances, whichever model performs better.
predictcan now write an extensive log file, and if that option is activated, as in production,
predictis a safe function that always completes; if there is an error, it returns a zero-row data frame that is otherwise the same as what would have been returned (provided
NAfor complication when the DRG is missing.
methodsis attached on attaching the package so that scripts operate the same in Rscript, R GUI, and R Studio.
A whole new architecture featuring a simpler API, more rigor under the hood, and attractive plots.
methodsto maintain functionality across environments
LassoDeploymentfor usage details
skip_on_not_appveyorwill skip a unit test unless it’s being run on Appveyor.
findVariaionwill return groups with the highest variation of a chosen target measure within a data set.
variationAcrossGroupswill plot a boxplot of variation between groups for a chosen target measure.
SupervisedModelDevelopmentnow saves the model after training
SupervisedModelDeploymentno longer trains models. It only loads the model saved in
SupervisedModelDevelopment. Predictions are made for all data.
imputeColumnwas replaced with
DBIbackend. We support reading and writing to MSSQL and SQLite databases.
getPredictions()in development (lasso, random forest, linear mixed model)