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      "title": "Subset of growth data from the collaborative perinatal project (CPP)",
      "topics": [
        "cpp",
        "cpp_imputed"
      ]
    },
    {
      "page": "cpp_1yr",
      "title": "Subset of growth data from the collaborative perinatal project (CPP)",
      "topics": [
        "cpp_1yr"
      ]
    },
    {
      "page": "Custom_chain",
      "title": "Customize chaining for a learner",
      "concept": [
        "Learners"
      ],
      "topics": [
        "customize_chain",
        "Custom_chain"
      ]
    },
    {
      "page": "cv_risk",
      "title": "Cross-validated Risk Estimation",
      "topics": [
        "cv_risk"
      ]
    },
    {
      "page": "cv_sl",
      "title": "Cross-validated Super Learner",
      "topics": [
        "cv_sl"
      ]
    },
    {
      "page": "debug_helpers",
      "title": "Helper functions to debug sl3 Learners",
      "topics": [
        "debugonce_predict",
        "debugonce_train",
        "debug_predict",
        "debug_train",
        "sl3_debug_mode",
        "undebug_learner"
      ]
    },
    {
      "page": "default_metalearner",
      "title": "Automatically Defined Metalearner",
      "topics": [
        "default_metalearner"
      ]
    },
    {
      "page": "Lrnr_h2o_glm",
      "title": "h2o Model Definition",
      "concept": [
        "Learners"
      ],
      "topics": [
        "define_h2o_X",
        "Lrnr_h2o_glm"
      ]
    },
    {
      "page": "learner_helpers",
      "title": "Learner helpers",
      "topics": [
        "delayed_learner_fit_chain",
        "delayed_learner_fit_predict",
        "delayed_learner_process_formula",
        "delayed_learner_subset_covariates",
        "delayed_learner_train",
        "delayed_make_learner",
        "learner_fit_chain",
        "learner_fit_predict",
        "learner_process_formula",
        "learner_subset_covariates",
        "learner_train"
      ]
    },
    {
      "page": "density_dat",
      "title": "Simulated data with continuous exposure",
      "topics": [
        "density_dat"
      ]
    },
    {
      "page": "factors_to_indicators",
      "title": "Convert Factors to indicators",
      "topics": [
        "dt_expand_factors",
        "factor_to_indicators"
      ]
    },
    {
      "page": "importance",
      "title": "Importance Extract variable importance measures produced by 'randomForest' and order in decreasing order of importance.",
      "topics": [
        "importance"
      ]
    },
    {
      "page": "importance_plot",
      "title": "Variable Importance Plot",
      "topics": [
        "importance_plot"
      ]
    },
    {
      "page": "inverse_sample",
      "title": "Inverse CDF Sampling",
      "topics": [
        "inverse_sample"
      ]
    },
    {
      "page": "loss_functions",
      "title": "Loss Function Definitions",
      "topics": [
        "loss_functions",
        "loss_loglik_binomial",
        "loss_loglik_multinomial",
        "loss_loglik_true_cat",
        "loss_squared_error",
        "loss_squared_error_multivariate"
      ]
    },
    {
      "page": "Lrnr_arima",
      "title": "Univariate ARIMA Models",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_arima"
      ]
    },
    {
      "page": "Lrnr_bartMachine",
      "title": "bartMachine: Bayesian Additive Regression Trees (BART)",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_bartMachine"
      ]
    },
    {
      "page": "Lrnr_base",
      "title": "Base Class for all sl3 Learners",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_base",
        "make_learner"
      ]
    },
    {
      "page": "Lrnr_bayesglm",
      "title": "Bayesian Generalized Linear Models",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_bayesglm"
      ]
    },
    {
      "page": "Lrnr_bound",
      "title": "Bound Predictions",
      "topics": [
        "Lrnr_bound"
      ]
    },
    {
      "page": "Lrnr_caret",
      "title": "Caret (Classification and Regression) Training",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_caret"
      ]
    },
    {
      "page": "Lrnr_cv",
      "title": "Fit/Predict a learner with Cross Validation",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_cv"
      ]
    },
    {
      "page": "Lrnr_cv_selector",
      "title": "Cross-Validated Selector",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_cv_selector"
      ]
    },
    {
      "page": "Lrnr_dbarts",
      "title": "Discrete Bayesian Additive Regression Tree sampler",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_dbarts"
      ]
    },
    {
      "page": "Lrnr_define_interactions",
      "title": "Define interactions terms",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_define_interactions"
      ]
    },
    {
      "page": "Lrnr_density_discretize",
      "title": "Density from Classification",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_density_discretize"
      ]
    },
    {
      "page": "Lrnr_density_hse",
      "title": "Density Estimation With Mean Model and Homoscedastic Errors",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_density_hse"
      ]
    },
    {
      "page": "Lrnr_density_semiparametric",
      "title": "Density Estimation With Mean Model and Homoscedastic Errors",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_density_semiparametric"
      ]
    },
    {
      "page": "Lrnr_earth",
      "title": "Earth: Multivariate Adaptive Regression Splines",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_earth"
      ]
    },
    {
      "page": "Lrnr_expSmooth",
      "title": "Exponential Smoothing state space model",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_expSmooth"
      ]
    },
    {
      "page": "Lrnr_ga",
      "title": "Nonlinear Optimization via Genetic Algorithm (GA)",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_ga"
      ]
    },
    {
      "page": "Lrnr_gam",
      "title": "GAM: Generalized Additive Models",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_gam"
      ]
    },
    {
      "page": "Lrnr_gbm",
      "title": "GBM: Generalized Boosted Regression Models",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_gbm"
      ]
    },
    {
      "page": "Lrnr_glm",
      "title": "Generalized Linear Models",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_glm"
      ]
    },
    {
      "page": "Lrnr_glm_fast",
      "title": "Computationally Efficient Generalized Linear Model (GLM) Fitting",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_glm_fast"
      ]
    },
    {
      "page": "Lrnr_glm_semiparametric",
      "title": "Semiparametric Generalized Linear Models",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_glm_semiparametric"
      ]
    },
    {
      "page": "Lrnr_glmnet",
      "title": "GLMs with Elastic Net Regularization",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_glmnet"
      ]
    },
    {
      "page": "Lrnr_glmtree",
      "title": "Generalized Linear Model Trees",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_glmtree"
      ]
    },
    {
      "page": "Lrnr_grf",
      "title": "Generalized Random Forests Learner",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_grf"
      ]
    },
    {
      "page": "Lrnr_grfcate",
      "title": "Generalized Random Forests for Conditional Average Treatment Effects",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_grfcate"
      ]
    },
    {
      "page": "Lrnr_gru_keras",
      "title": "Recurrent Neural Network with Gated Recurrent Unit (GRU) with Keras",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_gru_keras"
      ]
    },
    {
      "page": "Lrnr_h2o_grid",
      "title": "Grid Search Models with h2o",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_h2o_classifier",
        "Lrnr_h2o_grid",
        "Lrnr_h2o_mutator"
      ]
    },
    {
      "page": "Lrnr_hal9001",
      "title": "Scalable Highly Adaptive Lasso (HAL)",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_hal9001"
      ]
    },
    {
      "page": "Lrnr_haldensify",
      "title": "Conditional Density Estimation with the Highly Adaptive LASSO",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_haldensify"
      ]
    },
    {
      "page": "Lrnr_HarmonicReg",
      "title": "Harmonic Regression",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_HarmonicReg"
      ]
    },
    {
      "page": "Lrnr_independent_binomial",
      "title": "Classification from Binomial Regression",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_independent_binomial"
      ]
    },
    {
      "page": "Lrnr_lightgbm",
      "title": "LightGBM: Light Gradient Boosting Machine",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_lightgbm"
      ]
    },
    {
      "page": "Lrnr_lstm_keras",
      "title": "Long short-term memory Recurrent Neural Network (LSTM) with Keras",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_lstm_keras"
      ]
    },
    {
      "page": "Lrnr_mean",
      "title": "Fitting Intercept Models",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_mean"
      ]
    },
    {
      "page": "Lrnr_multiple_ts",
      "title": "Stratify univariable time-series learners by time-series",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_multiple_ts"
      ]
    },
    {
      "page": "Lrnr_multivariate",
      "title": "Multivariate Learner",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_multivariate"
      ]
    },
    {
      "page": "Lrnr_nnet",
      "title": "Feed-Forward Neural Networks and Multinomial Log-Linear Models",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_nnet"
      ]
    },
    {
      "page": "Lrnr_nnls",
      "title": "Non-negative Linear Least Squares",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_nnls"
      ]
    },
    {
      "page": "Lrnr_optim",
      "title": "Optimize Metalearner according to Loss Function using optim",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_optim"
      ]
    },
    {
      "page": "Lrnr_pca",
      "title": "Principal Component Analysis and Regression",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_pca"
      ]
    },
    {
      "page": "SuperLearner_interface",
      "title": "Use SuperLearner Wrappers, Screeners, and Methods, in sl3",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_pkg_SuperLearner",
        "Lrnr_pkg_SuperLearner_method",
        "Lrnr_pkg_SuperLearner_screener"
      ]
    },
    {
      "page": "Lrnr_polspline",
      "title": "Polyspline - multivariate adaptive polynomial spline regression (polymars) and polychotomous regression and multiple classification (polyclass)",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_polspline"
      ]
    },
    {
      "page": "Lrnr_pooled_hazards",
      "title": "Classification from Pooled Hazards",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_pooled_hazards"
      ]
    },
    {
      "page": "Lrnr_randomForest",
      "title": "Random Forests",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_randomForest"
      ]
    },
    {
      "page": "Lrnr_ranger",
      "title": "Ranger: Fast(er) Random Forests",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_ranger"
      ]
    },
    {
      "page": "Lrnr_revere_task",
      "title": "Learner that chains into a revere task",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_revere_task"
      ]
    },
    {
      "page": "Lrnr_rpart",
      "title": "Learner for Recursive Partitioning and Regression Trees",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_rpart"
      ]
    },
    {
      "page": "Lrnr_rugarch",
      "title": "Univariate GARCH Models",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_rugarch"
      ]
    },
    {
      "page": "Lrnr_screener_augment",
      "title": "Augmented Covariate Screener",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_screener_augment"
      ]
    },
    {
      "page": "Lrnr_screener_coefs",
      "title": "Coefficient Magnitude Screener",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_screener_coefs"
      ]
    },
    {
      "page": "Lrnr_screener_correlation",
      "title": "Correlation Screening Procedures",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_screener_correlation"
      ]
    },
    {
      "page": "Lrnr_screener_importance",
      "title": "Variable Importance Screener",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_screener_importance"
      ]
    },
    {
      "page": "Lrnr_sl",
      "title": "The Super Learner Algorithm",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_sl"
      ]
    },
    {
      "page": "Lrnr_solnp",
      "title": "Nonlinear Optimization via Augmented Lagrange",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_solnp"
      ]
    },
    {
      "page": "Lrnr_solnp_density",
      "title": "Nonlinear Optimization via Augmented Lagrange",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_solnp_density"
      ]
    },
    {
      "page": "Lrnr_stratified",
      "title": "Stratify learner fits by a single variable",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_stratified"
      ]
    },
    {
      "page": "Lrnr_subset_covariates",
      "title": "Learner with Covariate Subsetting",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_subset_covariates"
      ]
    },
    {
      "page": "Lrnr_svm",
      "title": "Support Vector Machines",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_svm"
      ]
    },
    {
      "page": "Lrnr_ts_weights",
      "title": "Time-specific weighting of prediction losses",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_ts_weights"
      ]
    },
    {
      "page": "Lrnr_tsDyn",
      "title": "Nonlinear Time Series Analysis",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_tsDyn"
      ]
    },
    {
      "page": "Lrnr_xgboost",
      "title": "xgboost: eXtreme Gradient Boosting",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Lrnr_xgboost"
      ]
    },
    {
      "page": "make_learner_stack",
      "title": "Make a stack of sl3 learners",
      "topics": [
        "make_learner_stack"
      ]
    },
    {
      "page": "metalearners",
      "title": "Combine predictions from multiple learners",
      "topics": [
        "metalearners",
        "metalearner_linear",
        "metalearner_linear_multinomial",
        "metalearner_linear_multivariate",
        "metalearner_logistic_binomial"
      ]
    },
    {
      "page": "pack_predictions",
      "title": "Pack multidimensional predictions into a vector (and unpack again)",
      "topics": [
        "normalize_rows",
        "pack_predictions",
        "print.packed_predictions",
        "unpack_predictions"
      ]
    },
    {
      "page": "Pipeline",
      "title": "Pipeline (chain) of learners.",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Pipeline"
      ]
    },
    {
      "page": "pooled_hazard_task",
      "title": "Generate A Pooled Hazards Task from a Failure Time (or Categorical) Task",
      "topics": [
        "pooled_hazard_task"
      ]
    },
    {
      "page": "predict_classes",
      "title": "Predict Class from Predicted Probabilities",
      "topics": [
        "predict_classes"
      ]
    },
    {
      "page": "prediction_plot",
      "title": "Plot predicted and true values for diganostic purposes",
      "topics": [
        "prediction_plot"
      ]
    },
    {
      "page": "process_data",
      "title": "Process Data",
      "topics": [
        "process_data"
      ]
    },
    {
      "page": "risk",
      "title": "Risk Estimation",
      "topics": [
        "risk"
      ]
    },
    {
      "page": "risk_functions",
      "title": "FACTORY RISK FUNCTION FOR ROCR PERFORMANCE MEASURES WITH BINARY OUTCOMES",
      "topics": [
        "custom_ROCR_risk",
        "risk_functions"
      ]
    },
    {
      "page": "safe_dim",
      "title": "dim that works for vectors too",
      "topics": [
        "safe_dim"
      ]
    },
    {
      "page": "Shared_Data",
      "title": "Container Class for data.table Shared Between Tasks",
      "topics": [
        "Shared_Data"
      ]
    },
    {
      "page": "list_learners",
      "title": "List sl3 Learners",
      "topics": [
        "sl3_list_learners",
        "sl3_list_properties"
      ]
    },
    {
      "page": "sl3_revere_Task",
      "title": "Revere (SplitSpecific) Task",
      "topics": [
        "sl3_revere_Task"
      ]
    },
    {
      "page": "sl3_Task",
      "title": "Define a Machine Learning Task",
      "topics": [
        "make_sl3_Task",
        "sl3_Task"
      ]
    },
    {
      "page": "sl3Options",
      "title": "Querying/setting a single 'sl3' option",
      "topics": [
        "sl3Options"
      ]
    },
    {
      "page": "Stack",
      "title": "Learner Stacking",
      "concept": [
        "Learners"
      ],
      "topics": [
        "Stack"
      ]
    },
    {
      "page": "subset_folds",
      "title": "Make folds work on subset of data",
      "topics": [
        "subset_folds"
      ]
    },
    {
      "page": "cv_helpers",
      "title": "Subset Tasks for CV THe functions use origami folds to subset tasks. These functions are used by Lrnr_cv (and therefore other learners that use Lrnr_cv). So that nested cv works properly, currently the subsetted task objects do not have fold structures of their own, and so generate them from defaults if nested cv is requested.",
      "topics": [
        "train_task",
        "validation_task"
      ]
    },
    {
      "page": "undocumented_learner",
      "title": "Undocumented Learner",
      "concept": [
        "Learners"
      ],
      "topics": [
        "undocumented_learner"
      ]
    },
    {
      "page": "variable_type",
      "title": "Specify Variable Type",
      "topics": [
        "Variable_Type",
        "variable_type"
      ]
    },
    {
      "page": "write_learner_template",
      "title": "Generate a file containing a template 'sl3' Learner",
      "topics": [
        "write_learner_template"
      ]
    }
  ],
  "_readme": "https://github.com/tlverse/sl3/raw/fix-tests/README.md",
  "_rundeps": [
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    "BBmisc",
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    "class",
    "cli",
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    "crayon",
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    "htmltools",
    "htmlwidgets",
    "igraph",
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    "iterators",
    "jquerylib",
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    "KernSmooth",
    "knitr",
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