Package: E2E 0.1.3

E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Provides a framework to build and evaluate diagnosis or prognosis models using stacking, voting, and bagging ensemble techniques with various base learners. The package also includes tools for visualization and interpretation of models. The development version of the package is available on 'GitHub' at <https://github.com/xiaojie0519/E2E>. The methods are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655> on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> on stacking.

Authors:Shanjie Luan [aut, cre]

E2E_0.1.3.tar.gz
E2E_0.1.3.zip(r-4.7)E2E_0.1.3.zip(r-4.6)E2E_0.1.3.zip(r-4.5)
E2E_0.1.3.tgz(r-4.6-any)E2E_0.1.3.tgz(r-4.5-any)
E2E_0.1.3.tar.gz(r-4.7-any)E2E_0.1.3.tar.gz(r-4.6-any)
E2E_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
E2E/json (API)

# Install 'E2E' in R:
install.packages('E2E', repos = c('https://xiaojie0519.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/xiaojie0519/e2e/issues

Pkgdown/docs site:https://xiaojie0519.github.io

Datasets:
  • test_dia - Test Data for Diagnostic Models
  • test_pro - Test Data for Prognostic (Survival) Models
  • train_dia - Training Data for Diagnostic Models
  • train_pro - Training Data for Prognostic (Survival) Models

On CRAN:

Conda:

6.90 score 24 stars 11 scripts 486 downloads 55 exports 218 dependencies

Last updated from:608bcfd714. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK322
source / vignettesOK647
linux-release-x86_64OK330
macos-release-arm64OK180
macos-oldrel-arm64OK263
windows-develOK223
windows-releaseOK224
windows-oldrelOK212
wasm-releaseOK245

Exports:apply_diaapply_probagging_diabagging_procalculate_metrics_at_threshold_diadt_diaen_diaen_proevaluate_model_diaevaluate_model_proevaluate_predictions_diaevaluate_predictions_profigure_diafigure_profigure_shapfind_optimal_threshold_diagbm_diagbm_proget_registered_models_diaget_registered_models_proimbalance_diainitialize_modeling_system_diainitialize_modeling_system_proint_diaint_imbalanceint_prolasso_dialasso_prolda_diaload_and_prepare_data_diamin_max_normalizemlp_diamodels_diamodels_pronb_diaplot_integrated_resultspls_propredict_proprint_model_summary_diaprint_model_summary_proqda_diaregister_model_diaregister_model_prorf_diaridge_diaridge_prorsf_prostacking_diastacking_prostepcox_proSurvsvm_diavoting_diaxb_diaxgb_pro

Dependencies:abindbackportsbase64encBHBiocParallelbipartitebitbit64bootbootstrapbroombslibcachemcarcarDatacaretcheckmateclassclicliprclockclustercodacodetoolscolorspacecommonmarkcorpcorcorrplotcowplotcpp11crayoncurldata.tabledata.treeDerivdiagramDiagrammeRdigestdoBydotCall64dplyre1071ellipseevaluateexactRankTestsfarverfastmapfieldsfontawesomeforeachforecastforeignformatRFormulafracdifffsfutile.loggerfutile.optionsfuturefuture.applygbmgenericsggfittextgggenesggplot2ggpubrggrepelggsciggsignifggtextglmnetglobalsgluegowergridExtragridtextgtablehardhathighrHmischmshtmlTablehtmltoolshtmlwidgetsigraphipredisobanditeratorsjpegjquerylibjsonlitekernlabKernSmoothknitrlabelinglambda.rlarslatticelavalifecyclelistenvlitedownlme4lmtestlubridatemagrittrmapsmarkdownMASSMatrixMatrixModelsmatrixStatsmaxstatmemoisemgcvmicrobenchmarkmimeminqamixOmicsModelMetricsmodelrmultcompmvtnormnetworknlmenloptrnnetnumDerivparallellypatchworkpbkrtestpermutepillarpkgconfigplsplsRcoxplsRglmplyrpngpolsplinepolynomprettyunitspROCprodlimprogressprogressrproxyPRROCpurrrquantregR6randomForestSRCrappdirsrARPACKrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrrecipesreformulasreshape2rglrisksetROCrlangrmarkdownrmetarmsrpartRSNNSRSpectrarstatixrstudioapiS7sandwichsassscalesshadesshapeshapvizsnasnowspamSparseMsparsevctrsSQUAREMstatnet.commonstringistringrSuppDistssurvAUCsurvcompsurvivalsurvivalROCsurvminerTH.datatibbletidyrtidyselecttimechangetimeDatetinytextzdburcautf8vctrsveganviridisLitevisNetworkvroomwithrxfunxgboostxml2yamlzoo

Diagnostic Workflow
Diagnostic Models (Classification) | 1. Initialization | 2. Training Single Models with models_dia | Basic Usage | Advanced Usage & Customization | 3. Ensemble Modeling | Bagging (bagging_dia) | Voting (voting_dia) | Stacking (stacking_dia) | Handling Imbalanced Data (imbalance_dia) | 4. Applying Models to New Data (apply_dia) | 5. Visualization (figure_dia)
Rendered fromdiagnostic-workflow.Rmdusingknitr::rmarkdown

Last update: 2025-12-04
Started: 2025-08-15

Advanced Features & Customization
Advanced Features | 1. Extending the Framework: Adding New Models | 2. Model Explanation with SHAP (figure_shap) | Explaining a Diagnostic Model | Explaining a Prognostic Model
Rendered fromadvanced-features.Rmdusingknitr::rmarkdown

Last update: 2025-12-04
Started: 2025-08-15

One-Click Run All Models
Overview | 1. Diagnostic Pipeline | 2. Imbalanced Data Pipeline | 3. Prognostic Pipeline | Key Features
Rendered fromintegrated-pipeline.Rmdusingknitr::rmarkdown

Last update: 2025-12-04
Started: 2025-11-23

Parameter Reference Guide
E2E Package Parameter Reference Guide | Built-in Datasets | Diagnostic Datasets | Prognostic Datasets | Built-in Models | Diagnostic Models (12 algorithms) | Prognostic Models (9 algorithms) | Integrated Pipeline Functions | int_dia() | int_pro() | plot_integrated_results() | Diagnostic Modeling Functions | models_dia() | bagging_dia() | voting_dia() | stacking_dia() | imbalance_dia() | apply_dia() | evaluate_predictions_dia() | Prognostic Modeling Functions | models_pro() | stacking_pro() | bagging_pro() | apply_pro() | evaluate_predictions_pro() | Visualization Functions | figure_dia() | figure_pro() | figure_shap() | Custom Model Registration | register_model_dia() / register_model_pro()
Rendered fromparameter-reference.Rmdusingknitr::rmarkdown

Last update: 2025-11-28
Started: 2025-09-02

Prognostic Workflow
Prognostic Models (Survival Analysis) | 1. Initialization | 2. Training Single Models with models_pro | 3. Ensemble Modeling | Bagging (bagging_pro) | Stacking (stacking_pro) | 4. Applying Models to New Data (apply_pro) | 5. Visualization (figure_pro)
Rendered fromprognostic-workflow.Rmdusingknitr::rmarkdown

Last update: 2025-09-02
Started: 2025-08-15

Getting Started
E2E: An R Package for Easy-to-Build Ensemble Models | Installation | Core Concepts | Sample Data
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2025-08-31
Started: 2025-08-15

Readme and manuals

Help Manual

Help pageTopics
Apply a Trained Model to New Dataapply_dia
Apply Prognostic Model to New Dataapply_pro
Train a Bagging Diagnostic Modelbagging_dia
Train Bagging Ensemble for Prognosisbagging_pro
Calculate Classification Metrics at a Specific Thresholdcalculate_metrics_at_threshold_dia
Train a Decision Tree Model for Classificationdt_dia
Train an Elastic Net (L1 and L2 Regularized Logistic Regression) Model for Classificationen_dia
Train Elastic Net Cox Modelen_pro
Evaluate Diagnostic Model Performanceevaluate_model_dia
Evaluate Prognostic Model Performanceevaluate_model_pro
Evaluate Predictions from a Data Frameevaluate_predictions_dia
Evaluate External Predictionsevaluate_predictions_pro
Plot Diagnostic Model Evaluation Figuresfigure_dia
Plot Prognostic Model Evaluation Figuresfigure_pro
Generate and Plot SHAP Explanation Figuresfigure_shap
Find Optimal Probability Thresholdfind_optimal_threshold_dia
Train a Gradient Boosting Machine (GBM) Model for Classificationgbm_dia
Train Gradient Boosting Machine (GBM) for Survivalgbm_pro
Get Registered Diagnostic Modelsget_registered_models_dia
Get Registered Prognostic Modelsget_registered_models_pro
Train an EasyEnsemble Model for Imbalanced Classificationimbalance_dia
Initialize Diagnostic Modeling Systeminitialize_modeling_system_dia
Initialize Prognosis Modeling Systeminitialize_modeling_system_pro
Comprehensive Diagnostic Modeling Pipelineint_dia
Imbalanced Data Diagnostic Modeling Pipelineint_imbalance
Comprehensive Prognostic Modeling Pipelineint_pro
Train a Lasso (L1 Regularized Logistic Regression) Model for Classificationlasso_dia
Train Lasso Cox Proportional Hazards Modellasso_pro
Train a Linear Discriminant Analysis (LDA) Model for Classificationlda_dia
Load and Prepare Data for Diagnostic Modelsload_and_prepare_data_dia
Min-Max Normalizationmin_max_normalize
Train a Multi-Layer Perceptron (Neural Network) Model for Classificationmlp_dia
Run Multiple Diagnostic Modelsmodels_dia
Run Multiple Prognostic Modelsmodels_pro
Train a Naive Bayes Model for Classificationnb_dia
Visualize Integrated Modeling Resultsplot_integrated_results
Train Partial Least Squares Cox (PLS-Cox)pls_pro
Generic Prediction Interface for Prognostic Modelspredict_pro
Print Diagnostic Model Summaryprint_model_summary_dia
Print Prognostic Model Summaryprint_model_summary_pro
Train a Quadratic Discriminant Analysis (QDA) Model for Classificationqda_dia
Register a Diagnostic Model Functionregister_model_dia
Register a Prognostic Modelregister_model_pro
Train a Random Forest Model for Classificationrf_dia
Train a Ridge (L2 Regularized Logistic Regression) Model for Classificationridge_dia
Train Ridge Cox Modelridge_pro
Train Random Survival Forest (RSF)rsf_pro
Train a Stacking Diagnostic Modelstacking_dia
Train Stacking Ensemble for Prognosisstacking_pro
Train Stepwise Cox Model (AIC-based)stepcox_pro
re-export Surv from survivalSurv
Train a Support Vector Machine (Linear Kernel) Model for Classificationsvm_dia
Test Data for Diagnostic Modelstest_dia
Test Data for Prognostic (Survival) Modelstest_pro
Training Data for Diagnostic Modelstrain_dia
Training Data for Prognostic (Survival) Modelstrain_pro
Train a Voting Ensemble Diagnostic Modelvoting_dia
Train an XGBoost Tree Model for Classificationxb_dia
Train XGBoost Cox Modelxgb_pro