NEWS.md

sl3 1.4.5

• Changed CV_lrnr_sl to cv_sl
• Added Lrnr_glmtree, which uses the partykit R package to fit recursive partitioning and regression trees in a generalized linear model.
• Added fold-specific SL coefficients to the output of cv_sl, and removed the coefficients column from the returned cv_risk table.
• Added get_sl_revere_risk argument to Lrnr_sl's cv_risk method to provide the option (with default of FALSE) to add a super learner's revere-based risk (not a true cross-validated risk) to cv_risk output.
• Changed default metalearner to Lrnr_nnls for binary and continuous outcomes.
• Added cv_control argument to Lrnr_sl, which allows users to define specific cross-validation structures for fitting the super learner. This is intended for use in a nested cross-validation scheme (such as cross-validated super learner, cv_sl, or when Lrnr_sl is considered in the list of candidate learners in another Lrnr_sl). In addition to constructing clustered cross-validation with respect to id, cv_control also can be used to construct stratified cross-validation folds for Lrnr_sl.
• Lrnr_caret now works for binary and categorical outcomes. Previous versions state that these discrete outcome types are supported by Lrnr_caret, but the functionality would brake.
• Added public function for sl3_Task, get_folds, which takes in origami::make_folds arguments and returns the folds. This function is now called by task$folds and it can be called in train as well, to obtain folds from a task that have a non-default fold structure.
• Learners that use CV internally (i.e., as part of their procedure to select tuning parameters), including Lrnr_caret, Lrnr_glmnet, Lrnr_hal9001, and Lrnr_sl, use task$get_folds to create folds. The learners' folds respect the default CV fold structure in sl3 tasks (clustered CV when id is supplied in the task; and stratified CV when outcomes are categorical or binary, and when id are nested in strata if id supplied to task). However, V can be modified according to the learner-specific parameters. (Lrnr_sl has a few extra CV tuning arguments, which are thoroughly documented in cv_control and modifications are only recommended for advanced use of Lrnr_sl.)
• Fixed learner parameter formula bug, which was causing formulas with "." to return an empty task, and therefore learners with these formulas to fail.
• Fixed bug in Lrnr_cv_selector metalearner, which was using the wrong folds to calculate the cross-validated risk estimate. This impacted Lrnr_cv_selector when eval_function was not a loss function, e.g. AUC. By calling task$folds on the metalearner's training task, we were deriving folds from the matrix of cross-validated predictions, and not using the folds for cross-validating the candidates. We now require the folds for cross- validating the candidates (i.e., the folds in task for training Lrnr_sl) to be supplied when Lrnr_cv_selector's eval_function is not a loss function.
• Lrnr_caret and Lrnr_rpart factor binary outcomes in their train methods, thereby considering a classification prediction problem. To avoid this behavior and consider a regression prediction problem with a binary outcome (e.g., to minimize the squared error or negative log likelihood loss in a binary outcome prediction problem), users can set factor_binary_outcome = FALSE when they instantiate the learner.
• Tasks can be created without an outcome. This comes in handy when creating a task that is used only for prediction, not for training, and leads to the task's outcome type being set to "none" if it's not supplied.
• When the variable type of the outcome (i.e., outcome_type) is necessary for a learner's predict method (e.g., if categorical outcome predictions need to be "packed" together), the outcome type in the training task should be used. That is, private$.training_outcome_type should be used to obtain the outcome type in a learner's predict method; the task supplied to predict should not be used. The following learners were referring to the task supplied to predict in order to retain the outcome type, and they were modified to use the training task's outcome type instead: Lrnr_svm, Lrnr_randomForest, Lrnr_ranger, Lrnr_rpart, Lrnr_polspline. The issue with pulling the outcome type from the task supplied to predict is that the outcome type of that task might be "none", if the outcome argument is not supplied to it.
• Updated the learner template (inst/templates/Lrnr_template.R) to reflect the new formatting guidelines for learner documentation.
• Updated documentation for sl3_Task parameters (man-roxygen/sl3_Task_extra.R). Specifically, drop_missing_outcome and flag were added; offset description was fixed; description of folds was added, including how to modify it and the default; and description of how the default cross-validation structure considers id and discrete (binary and categorical) outcome types to construct clustered and stratified cross-validation schemes, respectively, was added.
• Added documentation for the function process_data (R/process_data.R), which is called when instantiating a task, to process the covariates and identify missingness in the outcome.
• Added Lrnr_grfcate, a prediction function estimator for conditional average treatment effect (CATE), which uses the causal_forest function in grf package. This learner is intended for use in the tmle3mopttx package, where CATE estimation and prediction is required.
• Added flexibility and error handling to optional sl3_Task argument outcome_type. Either "binomial", "binary" or binomial() can be supplied for a binary outcome; "continuous","gaussian", or gaussian() for a continuous outcome; "categorical", "multinomial", or mutlinomial()" for a categorical outcome. As before, when outcome_type is not supplied, we will try to detect it from the outcome values. If the supplied outcome_type differs from the detected one, a warning is now thrown. If outcome_type is supplied but invalid, then an error is thrown upon sl3_Task instantiation, opposed to learner training.
• Cross-validated super learner (cv_sl) returns the cross-validated predictions for the super learner and its candidates.

sl3 1.4.4

• Updates to Lrnr_nnls to support binary outcomes, including support for convexity of the resultant model fit and warnings on prediction quality.
• Refined, clearer documentation for Lrnr_define_interactions
• Tweaks to Lrnr_bound to better support more flexible bounding for continuous outcomes (automatically setting a maximum of infinity).
• Changes to Lrnr_cv_selector to support improved computation of the CV-risk, averaging the risk strictly across validation/holdout sets.
• Bug fixes for Lrnr_earth (improving formals recognition), Lrnr_glmnet (allowing offsets), and Lrnr_caret (reformatting of arguments).

sl3 1.4.3

• Additional arguments for 'Keras' learners Lrnr_lstm_keras and Lrnr_gru_keras provide support for callback functions list and 2-layer networks. Default callbacks list provides early stopping criteria with respect to 'Keras' defaults and patience of 10 epochs. Also, these two 'Keras' learners now call args_to_list upon initialization, and set verbose argument according to options("keras.fit_verbose") or options("sl3.verbose").
• Update Lrnr_xgboost to support prediction tasks consisting of one observation (e.g., leave-one-out cross-validation).
• Update Lrnr_sl by adding a new private slot .cv_risk to store the risk estimates, using this to avoid unnecessary re-computation in the print method (the .cv_risk slot is populated on the first print call, and only ever re-printed thereafter).
• Update documentation of default_metalearner to use native markdown tables.
• Fix Lrnr_screener_importance's pairing of (a) covariates returned by the importance function with (b) covariates as they are defined in the task. This issue only arose when discrete covariates were automatically one-hot encoded upon task initiation (i.e., when colnames(task$X) != task$nodes$covariates).
• Reformat importance_plot to plot variables in decreasing order of importance, so most important variables are placed at the top of the dotchart.
• Enhanced functionality in sl3 task's add_interactions method to support interactions that involve factors. This method is most commonly used by Lrnr_define_interactions, which is intended for use with another learner (e.g., Lrnr_glmnet or Lrnr_glm) in a Pipeline.
• Modified Lrnr_gam formula (if not specified by user) to not use mgcv's default k=10 degrees of freedom for each smooth s term when there are less than k=10 degrees of freedom. This bypasses an mgcv::gam error, and tends to be relevant only for small n.
• Added options(java.parameters = "-Xmx2500m") and warning message when Lrnr_bartMachine is initialized, if this option has not already been set. This option was incorporated since the default RAM of 500MB for a Java virtual machine often errors due to memory issues with Lrnr_bartMachine.
• Incorporated stratify_cv argument in Lrnr_glmnet, which stratifies internal cross-validation folds such that binary outcome prevalence in training and validation folds roughly matches the prevalence in the training task.
• Incorporated min_screen argument Lrnr_screener_coefs, which tries to ensure that at least min_screen number of covariates are selected. If this argument is specified and the learner argument in Lrnr_screener_coefs is a Lrnr_glmnet, then lambda is increased until min_screen number of covariates are selected and a warning is produced. If min_screen is specified and the learner argument in Lrnr_screener_coefs is not a Lrnr_glmnet then it will error.
• Updated Lrnr_hal9001 to work with v0.4.0 of the hal9001 package.
• Added formula parameter and process_formula function to the base learner, Lrnr_base, whose methods carry over to all other learners. When a formula is supplied as a learner parameter, the process_formula function constructs a design matrix by supplying the formulatomodel.matrix. This implementation allows formulato be supplied to all learners, even those without nativeformulasupport. Theformula should be an object of class "formula`", or a character string that can be coerced to that class.
• Added factory function for performance-based risks for binary outcomes with ROCR performance measures custom_ROCR_risk. Supports cutoff-dependent and scalar ROCR performance measures. The risk is defined as 1 - performance, and is transformed back to the performance measure in cv_risk and importance functions. This change prompted the revision of argument name loss_fun and loss_function to eval_fun and eval_function, respectively, since the evaluation of predictions relative to the observations can be either a risk or a loss function. This argument name change impacted the following: Lrnr_solnp, Lrnr_optim, Lrnr_cv_selector, cv_risk, importance, and CV_Lrnr_sl.
• Added name attribute to all loss functions, where naming was defined in terms of the risk implied by each loss function (i.e., the common name for the expected loss). The names in cv_risk and importance tables now swap "risk" with this name attribute.
• Incorporated stratified cross-validation when folds are not supplied to the sl3_Task and the outcome is a discrete (i.e., binary or categorical) variable.
• Added to the importance method the option to evaluate importance over covariate_groups, by removing/permuting all covariates in the same group together.
• Added Lrnr_ga as another metalearner.

sl3 1.4.2

• Updates to variable importance functionality, including calculation of risk ratio and risk differences under covariate deletion or permutation.
• Addition of a importance_plot to summarize variable importance findings.
• Additions of new methods reparameterize and retrain to Lrnr_base, which allows modification of the covariate set while training on a conserved task and prediction on a new task using previously trained learners, respectively.

sl3 1.4.1

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sl3 1.4.0

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sl3 1.3.9

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sl3 1.3.8

• Updates to variable importance functionality, including use of risk ratios.
• Change Lrnr_hal9001 and Lrnr_glmnet to respect observation-level IDs.
• Removal of Remotes and deprecation of Lrnr_rfcde and Lrnr_condensier:
  • Both of these learner classes provided support for conditional density estimation (CDE) and were useful when support for CDE was more limited. Unfortunately, both packages are un-maintained or updated only very sporadically, resulting in both frequent bugs and presenting an obstacle for an eventual CRAN release (both packages are GitHub-only).
  • Lrnr_rfcde wrapped https://github.com/tpospisi/RFCDE, a sporadically maintained tool for conditional density estimation (CDE). Support for this has been removed in favor of built-in CDE tools, including, among others, Lrnr_density_semiparametric.
  • Lrnr_condensier wrapped https://github.com/osofr/condensier, which provided a pooled hazards approach to CDE. This package contained an implementation error (osofr/condensier#15) and was removed from CRAN. Support for this has been removed in favor of Lrnr_density_semiparametric and Lrnr_haldensify, both of which more reliably provide CDE support.

sl3 1.3.7

• Sampling methods for Monte Carlo integration and related procedures.
• A metalearner for the cross-validation selector (discrete super learner).
• A learner for bounding, including support for bounded losses.
• Resolution of a number of older issues (see #264).
• Relaxation of checks inside Stack objects for time series learners.
• Addition of a learner property table to README.Rmd.
• Maintenance and documentation updates.

sl3 1.3.5

• Overhaul of data preprocessing.
• New screening methods and convex combination in Lrnr_nnls.
• Bug fixes, including covariate subsetting and better handling of NAs.
• Package and documentation cleanup; continuous integration and testing fixes.
• Reproducibility updates (including new versioning and DOI minting).

sl3 1.3.0

• Fixes incorrect handling of missingness in the automatic imputation procedure.
• Adds new standard learners, including from the gam and caret packages.
• Adds custom learners for conditional density estimation, including semiparametric methods based on conditional mean and conditional mean/variance estimation as well as generalized functionality for density estimation via a pooled hazards approach.

sl3 1.2.0

• Default metalearners based on task outcome types.
• Handling of imputation internally in task objects.
• Addition of several new learners, including from the gbm, earth, polspline packages.
• Fixing errors in existing learners (e.g., subtle parallelization in xgboost and ranger).
• Support for multivariate outcomes
• Sets default cross-validation to be revere-style.
• Support for cross-validated super learner and variable importance.

sl3 1.1.0

• A full-featured and stable release of the project.
• Numerous learners are included and many bugs have been fixed relative to earlier versions (esp v1.0.0) of the software.

sl3 1.0.0

• An initial stable release.