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<!-- Generated by pkgdown: do not edit by hand -->
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<h1>Reference</h1>
</div>
<table class="ref-index">
<colgroup>
<col class="alias" />
<col class="title" />
</colgroup>
<tbody>
<tr>
<th colspan="2">
<h2 id="section-defining-a-task" class="hasAnchor"><a href="#section-defining-a-task" class="anchor"></a>Defining A Task</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="sl3_Task.html">make_sl3_Task()</a></code> </p>
</td>
<td><p>Define a Machine Learning Task</p></td>
</tr><tr>
<td>
<p><code><a href="variable_type.html">variable_type()</a></code> </p>
</td>
<td><p>Specify Variable Type</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-finding-learners" class="hasAnchor"><a href="#section-finding-learners" class="anchor"></a>Finding Learners</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="list_learners.html">sl3_list_properties()</a></code> <code><a href="list_learners.html">sl3_list_learners()</a></code> </p>
</td>
<td><p>List sl3 Learners</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-sl-learners" class="hasAnchor"><a href="#section-sl-learners" class="anchor"></a>sl3 Learners</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="Lrnr_HarmonicReg.html">Lrnr_HarmonicReg</a></code> </p>
</td>
<td><p>Harmonic Regression</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_arima.html">Lrnr_arima</a></code> </p>
</td>
<td><p>Univariate ARIMA Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_bartMachine.html">Lrnr_bartMachine</a></code> </p>
</td>
<td><p>bartMachine: Bayesian Additive Regression Trees (BART)</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_base.html">make_learner()</a></code> </p>
</td>
<td><p>Base Class for all sl3 Learners</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_bayesglm.html">Lrnr_bayesglm</a></code> </p>
</td>
<td><p>Bayesian Generalized Linear Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_bilstm.html">Lrnr_bilstm</a></code> </p>
</td>
<td><p>Bidirectional Long short-term memory Recurrent Neural Network (LSTM)</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_bound.html">Lrnr_bound</a></code> </p>
</td>
<td><p>Bound Predictions</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_caret.html">Lrnr_caret</a></code> </p>
</td>
<td><p>Wrapping Learner for Package Caret</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_cv.html">Lrnr_cv</a></code> </p>
</td>
<td><p>Fit/Predict a learner with Cross Validation</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_cv_selector.html">Lrnr_cv_selector</a></code> </p>
</td>
<td><p>Cross-Validated Selector</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_dbarts.html">Lrnr_dbarts</a></code> </p>
</td>
<td><p>Discrete Bayesian Additive Regression Tree sampler</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_define_interactions.html">Lrnr_define_interactions</a></code> </p>
</td>
<td><p>Define interactions terms</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_density_discretize.html">Lrnr_density_discretize</a></code> </p>
</td>
<td><p>Density from Classification</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_density_hse.html">Lrnr_density_hse</a></code> </p>
</td>
<td><p>Density Estimation With Mean Model and Homoscedastic Errors</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_density_semiparametric.html">Lrnr_density_semiparametric</a></code> </p>
</td>
<td><p>Density Estimation With Mean Model and Homoscedastic Errors</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_earth.html">Lrnr_earth</a></code> </p>
</td>
<td><p>Earth: Multivariate Adaptive Regression Splines</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_expSmooth.html">Lrnr_expSmooth</a></code> </p>
</td>
<td><p>Exponential Smoothing state space model</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_ga.html">Lrnr_ga</a></code> </p>
</td>
<td><p>Nonlinear Optimization via Genetic Algorithm (GA)</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_gam.html">Lrnr_gam</a></code> </p>
</td>
<td><p>GAM: Generalized Additive Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_gbm.html">Lrnr_gbm</a></code> </p>
</td>
<td><p>GBM: Generalized Boosted Regression Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_glm.html">Lrnr_glm</a></code> </p>
</td>
<td><p>Generalized Linear Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_glm_fast.html">Lrnr_glm_fast</a></code> </p>
</td>
<td><p>Computationally Efficient Generalized Linear Model (GLM) Fitting</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_glmnet.html">Lrnr_glmnet</a></code> </p>
</td>
<td><p>GLMs with Elastic Net Regularization</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_grf.html">Lrnr_grf</a></code> </p>
</td>
<td><p>Generalized Random Forests Learner</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_gru_keras.html">Lrnr_gru_keras</a></code> </p>
</td>
<td><p>Recurrent Neural Network with Gated Recurrent Unit (GRU) with Keras</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_gts.html">Lrnr_gts</a></code> </p>
</td>
<td><p>Grouped Time-Series Forecasting</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_h2o_glm.html">define_h2o_X()</a></code> </p>
</td>
<td><p>h2o Model Definition</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_h2o_grid.html">Lrnr_h2o_grid</a></code> </p>
</td>
<td><p>Grid Search Models with h2o</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_hal9001.html">Lrnr_hal9001</a></code> </p>
</td>
<td><p>Scalable Highly Adaptive Lasso (HAL)</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_haldensify.html">Lrnr_haldensify</a></code> </p>
</td>
<td><p>Conditional Density Estimation with the Highly Adaptive LASSO</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_hts.html">Lrnr_hts</a></code> </p>
</td>
<td><p>Hierarchical Time-Series Forecasting</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_independent_binomial.html">Lrnr_independent_binomial</a></code> </p>
</td>
<td><p>Classification from Binomial Regression</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_lightgbm.html">Lrnr_lightgbm</a></code> </p>
</td>
<td><p>LightGBM: Light Gradient Boosting Machine</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_lstm_keras.html">Lrnr_lstm_keras</a></code> </p>
</td>
<td><p>Long short-term memory Recurrent Neural Network (LSTM) with Keras</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_mean.html">Lrnr_mean</a></code> </p>
</td>
<td><p>Fitting Intercept Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_multiple_ts.html">Lrnr_multiple_ts</a></code> </p>
</td>
<td><p>Stratify univariable time-series learners by time-series</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_multivariate.html">Lrnr_multivariate</a></code> </p>
</td>
<td><p>Multivariate Learner</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_nnet.html">Lrnr_nnet</a></code> </p>
</td>
<td><p>Feed-Forward Neural Networks and Multinomial Log-Linear Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_nnls.html">Lrnr_nnls</a></code> </p>
</td>
<td><p>Non-negative Linear Least Squares</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_optim.html">Lrnr_optim</a></code> </p>
</td>
<td><p>Optimize Metalearner according to Loss Function using optim</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_pca.html">Lrnr_pca</a></code> </p>
</td>
<td><p>Principal Component Analysis and Regression</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_polspline.html">Lrnr_polspline</a></code> </p>
</td>
<td><p>Polyspline - multivariate adaptive polynomial spline regression (polymars)
and polychotomous regression and multiple classification (polyclass)</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_pooled_hazards.html">Lrnr_pooled_hazards</a></code> </p>
</td>
<td><p>Classification from Pooled Hazards</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_randomForest.html">Lrnr_randomForest</a></code> </p>
</td>
<td><p>Random Forests</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_ranger.html">Lrnr_ranger</a></code> </p>
</td>
<td><p>Ranger: Fast(er) Random Forests</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_revere_task.html">Lrnr_revere_task</a></code> </p>
</td>
<td><p>Learner that chains into a revere task</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_rpart.html">Lrnr_rpart</a></code> </p>
</td>
<td><p>Learner for Recursive Partitioning and Regression Trees.</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_rugarch.html">Lrnr_rugarch</a></code> </p>
</td>
<td><p>Univariate GARCH Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_screener_augment.html">Lrnr_screener_augment</a></code> </p>
</td>
<td><p>Augmented Covariate Screener</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_screener_coefs.html">Lrnr_screener_coefs</a></code> </p>
</td>
<td><p>Coefficient Magnitude Screener</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_screener_correlation.html">Lrnr_screener_correlation</a></code> </p>
</td>
<td><p>Correlation Screening Procedures</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_screener_importance.html">Lrnr_screener_importance</a></code> </p>
</td>
<td><p>Variable Importance Screener</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_sl.html">Lrnr_sl</a></code> </p>
</td>
<td><p>The Super Learner Algorithm</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_solnp.html">Lrnr_solnp</a></code> </p>
</td>
<td><p>Nonlinear Optimization via Augmented Lagrange</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_solnp_density.html">Lrnr_solnp_density</a></code> </p>
</td>
<td><p>Nonlinear Optimization via Augmented Lagrange</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_stratified.html">Lrnr_stratified</a></code> </p>
</td>
<td><p>Stratify learner fits by a single variable</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_subset_covariates.html">Lrnr_subset_covariates</a></code> </p>
</td>
<td><p>Learner with Covariate Subsetting</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_svm.html">Lrnr_svm</a></code> </p>
</td>
<td><p>Support Vector Machines</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_tsDyn.html">Lrnr_tsDyn</a></code> </p>
</td>
<td><p>Nonlinear Time Series Analysis</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_ts_weights.html">Lrnr_ts_weights</a></code> </p>
</td>
<td><p>Time-specific weighting of prediction losses</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_xgboost.html">Lrnr_xgboost</a></code> </p>
</td>
<td><p>xgboost: eXtreme Gradient Boosting</p></td>
</tr><tr>
<td>
<p><code><a href="SuperLearner_interface.html">Lrnr_pkg_SuperLearner</a></code> </p>
</td>
<td><p>Use SuperLearner Wrappers, Screeners, and Methods, in sl3</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-composing-learners" class="hasAnchor"><a href="#section-composing-learners" class="anchor"></a>Composing Learners</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="Pipeline.html">Pipeline</a></code> </p>
</td>
<td><p>Pipeline (chain) of learners.</p></td>
</tr><tr>
<td>
<p><code><a href="Stack.html">Stack</a></code> </p>
</td>
<td><p>Learner Stacking</p></td>
</tr><tr>
<td>
<p><code><a href="Custom_chain.html">customize_chain()</a></code> </p>
</td>
<td><p>Customize chaining for a learner</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-loss-functions" class="hasAnchor"><a href="#section-loss-functions" class="anchor"></a>Loss functions</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="loss_functions.html">loss_squared_error()</a></code> <code><a href="loss_functions.html">loss_loglik_true_cat()</a></code> <code><a href="loss_functions.html">loss_loglik_binomial()</a></code> <code><a href="loss_functions.html">loss_loglik_multinomial()</a></code> <code><a href="loss_functions.html">loss_squared_error_multivariate()</a></code> </p>
</td>
<td><p>Loss Function Definitions</p></td>
</tr><tr>
<td>
<p><code><a href="risk.html">risk()</a></code> </p>
</td>
<td><p>Risk Estimation</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-metalearner-functions" class="hasAnchor"><a href="#section-metalearner-functions" class="anchor"></a>Metalearner functions</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="metalearners.html">metalearner_logistic_binomial()</a></code> <code><a href="metalearners.html">metalearner_linear()</a></code> <code><a href="metalearners.html">metalearner_linear_multivariate()</a></code> <code><a href="metalearners.html">metalearner_linear_multinomial()</a></code> </p>
</td>
<td><p>Combine predictions from multiple learners</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-helpful-for-defining-learners" class="hasAnchor"><a href="#section-helpful-for-defining-learners" class="anchor"></a>Helpful for Defining Learners</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="write_learner_template.html">write_learner_template()</a></code> </p>
</td>
<td><p>Generate a file containing a template <code>sl3</code> Learner</p></td>
</tr><tr>
<td>
<p><code><a href="args_to_list.html">args_to_list()</a></code> </p>
</td>
<td><p>Get all arguments of parent call (both specified and defaults) as list</p></td>
</tr><tr>
<td>
<p><code><a href="call_with_args.html">call_with_args()</a></code> </p>
</td>
<td><p>Call with filtered argument list</p></td>
</tr><tr>
<td>
<p><code><a href="true_obj_size.html">true_obj_size()</a></code> </p>
</td>
<td><p>Estimate object size using serialization</p></td>
</tr><tr>
<td>
<p><code><a href="safe_dim.html">safe_dim()</a></code> </p>
</td>
<td><p>dim that works for vectors too</p></td>
</tr><tr>
<td>
<p><code><a href="learner_helpers.html">delayed_make_learner()</a></code> <code><a href="learner_helpers.html">learner_train()</a></code> <code><a href="learner_helpers.html">delayed_learner_train()</a></code> <code><a href="learner_helpers.html">learner_fit_predict()</a></code> <code><a href="learner_helpers.html">delayed_learner_fit_predict()</a></code> <code><a href="learner_helpers.html">learner_fit_chain()</a></code> <code><a href="learner_helpers.html">delayed_learner_fit_chain()</a></code> <code><a href="learner_helpers.html">learner_subset_covariates()</a></code> <code><a href="learner_helpers.html">learner_process_formula()</a></code> <code><a href="learner_helpers.html">delayed_learner_subset_covariates()</a></code> <code><a href="learner_helpers.html">delayed_learner_process_formula()</a></code> </p>
</td>
<td><p>Learner helpers</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-sample-datasets" class="hasAnchor"><a href="#section-sample-datasets" class="anchor"></a>Sample Datasets</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="cpp.html">cpp</a></code> <code><a href="cpp.html">cpp_imputed</a></code> </p>
</td>
<td><p>Subset of growth data from the collaborative perinatal project (CPP)</p></td>
</tr><tr>
<td>
<p><code><a href="cpp_1yr.html">cpp_1yr</a></code> </p>
</td>
<td><p>Subset of growth data from the collaborative perinatal project (CPP)</p></td>
</tr><tr>
<td>
<p><code><a href="bsds.html">bsds</a></code> </p>
</td>
<td><p>Bicycle sharing time series dataset</p></td>
</tr><tr>
<td>
<p><code><a href="density_dat.html">density_dat</a></code> </p>
</td>
<td><p>Simulated data with continuous exposure</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-miscellaneous" class="hasAnchor"><a href="#section-miscellaneous" class="anchor"></a>Miscellaneous</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="sl3Options.html">sl3Options()</a></code> </p>
</td>
<td><p>Querying/setting a single <code>sl3</code> option</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-index" class="hasAnchor"><a href="#section-index" class="anchor"></a>Index</h2>
<p class="section-desc"></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p><code><a href="CV_lrnr_sl.html">CV_lrnr_sl()</a></code> </p>
</td>
<td><p>Estimates cross-validated risk of the Super Learner</p></td>
</tr><tr>
<td>
<p><code><a href="Custom_chain.html">customize_chain()</a></code> </p>
</td>
<td><p>Customize chaining for a learner</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_HarmonicReg.html">Lrnr_HarmonicReg</a></code> </p>
</td>
<td><p>Harmonic Regression</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_arima.html">Lrnr_arima</a></code> </p>
</td>
<td><p>Univariate ARIMA Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_bartMachine.html">Lrnr_bartMachine</a></code> </p>
</td>
<td><p>bartMachine: Bayesian Additive Regression Trees (BART)</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_base.html">make_learner()</a></code> </p>
</td>
<td><p>Base Class for all sl3 Learners</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_bayesglm.html">Lrnr_bayesglm</a></code> </p>
</td>
<td><p>Bayesian Generalized Linear Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_bilstm.html">Lrnr_bilstm</a></code> </p>
</td>
<td><p>Bidirectional Long short-term memory Recurrent Neural Network (LSTM)</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_bound.html">Lrnr_bound</a></code> </p>
</td>
<td><p>Bound Predictions</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_caret.html">Lrnr_caret</a></code> </p>
</td>
<td><p>Wrapping Learner for Package Caret</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_cv.html">Lrnr_cv</a></code> </p>
</td>
<td><p>Fit/Predict a learner with Cross Validation</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_cv_selector.html">Lrnr_cv_selector</a></code> </p>
</td>
<td><p>Cross-Validated Selector</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_dbarts.html">Lrnr_dbarts</a></code> </p>
</td>
<td><p>Discrete Bayesian Additive Regression Tree sampler</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_define_interactions.html">Lrnr_define_interactions</a></code> </p>
</td>
<td><p>Define interactions terms</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_density_discretize.html">Lrnr_density_discretize</a></code> </p>
</td>
<td><p>Density from Classification</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_density_hse.html">Lrnr_density_hse</a></code> </p>
</td>
<td><p>Density Estimation With Mean Model and Homoscedastic Errors</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_density_semiparametric.html">Lrnr_density_semiparametric</a></code> </p>
</td>
<td><p>Density Estimation With Mean Model and Homoscedastic Errors</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_earth.html">Lrnr_earth</a></code> </p>
</td>
<td><p>Earth: Multivariate Adaptive Regression Splines</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_expSmooth.html">Lrnr_expSmooth</a></code> </p>
</td>
<td><p>Exponential Smoothing state space model</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_ga.html">Lrnr_ga</a></code> </p>
</td>
<td><p>Nonlinear Optimization via Genetic Algorithm (GA)</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_gam.html">Lrnr_gam</a></code> </p>
</td>
<td><p>GAM: Generalized Additive Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_gbm.html">Lrnr_gbm</a></code> </p>
</td>
<td><p>GBM: Generalized Boosted Regression Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_glm.html">Lrnr_glm</a></code> </p>
</td>
<td><p>Generalized Linear Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_glm_fast.html">Lrnr_glm_fast</a></code> </p>
</td>
<td><p>Computationally Efficient Generalized Linear Model (GLM) Fitting</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_glmnet.html">Lrnr_glmnet</a></code> </p>
</td>
<td><p>GLMs with Elastic Net Regularization</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_grf.html">Lrnr_grf</a></code> </p>
</td>
<td><p>Generalized Random Forests Learner</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_gru_keras.html">Lrnr_gru_keras</a></code> </p>
</td>
<td><p>Recurrent Neural Network with Gated Recurrent Unit (GRU) with Keras</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_gts.html">Lrnr_gts</a></code> </p>
</td>
<td><p>Grouped Time-Series Forecasting</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_h2o_glm.html">define_h2o_X()</a></code> </p>
</td>
<td><p>h2o Model Definition</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_h2o_grid.html">Lrnr_h2o_grid</a></code> </p>
</td>
<td><p>Grid Search Models with h2o</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_hal9001.html">Lrnr_hal9001</a></code> </p>
</td>
<td><p>Scalable Highly Adaptive Lasso (HAL)</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_haldensify.html">Lrnr_haldensify</a></code> </p>
</td>
<td><p>Conditional Density Estimation with the Highly Adaptive LASSO</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_hts.html">Lrnr_hts</a></code> </p>
</td>
<td><p>Hierarchical Time-Series Forecasting</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_independent_binomial.html">Lrnr_independent_binomial</a></code> </p>
</td>
<td><p>Classification from Binomial Regression</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_lightgbm.html">Lrnr_lightgbm</a></code> </p>
</td>
<td><p>LightGBM: Light Gradient Boosting Machine</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_lstm_keras.html">Lrnr_lstm_keras</a></code> </p>
</td>
<td><p>Long short-term memory Recurrent Neural Network (LSTM) with Keras</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_mean.html">Lrnr_mean</a></code> </p>
</td>
<td><p>Fitting Intercept Models</p></td>
</tr><tr>
<td>
<p><code><a href="Lrnr_multiple_ts.html">Lrnr_multiple_ts</a></code> </p>
</td>