initial support for GLRM imputation
prauc_table
auc_table - default null hypothesis is now the highest AUC.
csvl_auc_table() - table of AUCs for each learner, plus Discrete SL and SL.
factors_to_indicators
allow missing values in factors
h2o_init_multinode - create a multi-node h2o cluster e.g. on a SLURM system.
impute_missing_values
support integer64 numeric type (data.table)
impute_missing_values
support supplying imputation values from another dataset, for use when scoring a model to a larger dataset.
load_all_code() - changed default environment from baseenv() to .GlobalEnv, which will make explicit package references unnecessary.
missingness_indicators - return matrix of integers rather than numerics.
set_java_memory() - specify maximum memory usage for rJava JVM.
get_java_memory() - get maximum memory allocated to a rJava JVM.
Mode - option to not choose NA as a mode, set TRUE by default.
plot_roc - plot the best learner, not necessarily the SuperLearner.
sl_h2o_auto - automatic machine learning via h2o.
sl_mgcv - add mgcv wrapper for splines.
sl_xgboost_cv - integrate support for gaussian family outcomes.
vim_corr - new function to rank covariates by their (possibly weighted) univariate correlation with the outcome.
don't drop matrix to a vector, e.g. when using analyzing a single covariate.
impute_missing_values
fix missingness indicators when no indicators need to be created.
missingness_indicators
Fix when only one column of missingness indicators needs to be created.
plot_roc - fix axis labels for updated ggplot2.
Initial release on CRAN.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.