E2E: An R Package for Easy-to-Build Ensemble Models
E2E is a comprehensive R package designed to streamline the development, evaluation, and interpretation of machine learning models for both diagnostic (classification) and prognostic (survival analysis) tasks. It provides a robust, extensible framework for training individual models and building powerful ensembles—including Bagging, Voting, and Stacking—with minimal code. The package also includes integrated tools for visualization and model explanation via SHAP values.
Author: Shanjie Luan (ORCID: 0009-0002-8569-8526)
Citation: If you use E2E in your research, please cite it as: “Shanjie Luan (2025). E2E: An R Package for Easy-to-Build Ensemble Models. https://github.com/XIAOJIE0519/E2E”
Note: The article is in the process of being written/submitted and is undergoing review by CRAN and further revisions. If you have any questions, please contact Luan20050519@163.com.
Installation
The development version of E2E can be installed directly from GitHub
using remotes.
# If you don't have remotes, install it first:
# install.packages("remotes")
remotes::install_github("XIAOJIE0519/E2E")After installation, load the package into your R session:
Core Concepts
E2E operates on two parallel tracks: Diagnostic Models and Prognostic Models. Before using functions from either track, you must initialize the corresponding system. This step registers a suite of pre-defined, commonly used models.
Sample Data
To follow the examples, you’ll need sample data files. There are four
data frames included in the package for you to try:
train_dia, test_dia, train_pro,
test_pro.
train_dia and test_dia are for diagnosis,
with column names sample, outcome, variable 1, 2, 3.
head(train_dia)
#> sample outcome AC009242.1 AC004637.1 AC246817.1
#> 1 TCGA-BH-A201-01A-11R-A14M-07 1 0.3988 1.3971 0.6180
#> 2 TCGA-C8-A12P-01A-11R-A115-07 1 0.0220 0.0000 0.0916
#> 3 TCGA-BH-A0W3-01A-11R-A109-07 1 0.0367 0.0000 0.0509
#> 4 TCGA-BH-A0H6-01A-21R-A056-07 1 0.1338 0.1563 0.0619
#> 5 TCGA-D8-A27V-01A-12R-A17B-07 1 0.1299 0.5056 0.0134
#> 6 TCGA-D8-A27M-01A-11R-A16F-07 1 0.1722 8.4883 0.2478
#> AL139241.1 PRDM16.DT LINC01028 LINC00639 AL135841.1 HYMAI KCNIP2.AS1
#> 1 0.1441 0.2706 0.0000 0.2019 0.4235 0.0000 0.2728
#> 2 0.0530 0.0221 0.0000 0.0393 0.0000 0.0000 0.0502
#> 3 0.0883 0.0921 0.0000 0.2729 0.0000 0.0000 0.2508
#> 4 0.0000 0.2555 0.0000 0.0558 0.0000 0.0000 0.3662
#> 5 0.6259 0.1378 0.0349 0.2062 0.2299 0.0849 0.4279
#> 6 2.6272 0.1730 0.0926 0.4101 0.4063 0.0483 0.3490
#> LINC00922 LINC01614 LINC01644 AC104237.3 AC016735.1 AC090125.1 AC008459.1
#> 1 0.8689 48.5811 4.1185 0.6571 4.9722 1.7074 0.5958
#> 2 1.6513 33.5412 0.0000 0.0000 0.0000 0.5024 0.0000
#> 3 2.3520 12.8974 0.1893 0.0000 0.0000 0.4186 0.0498
#> 4 1.8789 86.9410 0.0000 0.0000 0.0000 1.0694 0.0000
#> 5 0.6987 14.7284 3.5769 0.0951 1.1995 0.0824 0.0196
#> 6 0.9262 78.2584 0.0494 0.0000 6.3601 0.8736 0.0000
#> LINC00958 AC112721.2 LINC00924
#> 1 8.5194 2.4894 0.3138
#> 2 0.3649 2.2892 0.1656
#> 3 0.2146 0.5722 0.1003
#> 4 0.0392 3.7589 0.2381
#> 5 0.8306 0.9759 0.4740
#> 6 0.1493 9.6523 0.1571train_pro and test_pro are for prognosis,
with column names sample, outcome, time, variable 1, 2, 3.
head(train_pro)
#> sample outcome time LINC01497 LINC01028 AC084212.1
#> 1 TCGA-AC-A7VC-01A-11R-A352-07 0 1 0.0000 0 0.0000
#> 2 TCGA-C8-A275-01A-21R-A16F-07 0 1 0.0000 0 0.0000
#> 3 TCGA-C8-A1HJ-01A-11R-A13Q-07 0 5 0.1135 0 0.0298
#> 4 TCGA-PL-A8LX-01A-11R-A41B-07 0 5 0.0000 0 0.0000
#> 5 TCGA-AN-A041-01A-11R-A034-07 0 7 0.0000 0 0.0000
#> 6 TCGA-PL-A8LY-01A-11R-A41B-07 0 8 0.1910 0 0.0000
#> AC104211.1 AL603840.1 AL590434.1 AC110772.2 LINC01614 C9orf163 AL391845.2
#> 1 0.4532 0.1294 0.0000 0.3130 224.7098 0.1968 1.1866
#> 2 0.0168 0.0000 0.0498 0.0000 25.0775 1.3142 0.4553
#> 3 0.0543 0.4426 0.0603 0.4218 11.4685 0.4421 0.2389
#> 4 0.5331 0.0000 0.0000 0.0000 4.8058 2.4996 2.9862
#> 5 0.3376 0.0000 0.0000 0.0343 77.7571 0.7764 1.7484
#> 6 0.1218 0.5118 0.9477 0.0526 0.0000 0.8931 0.0000
#> HYMAI LINC01152 AL133467.1 LINC00165 LINC02408 AC092118.1 AP000851.2
#> 1 0.4287 0.4054 0.4250 0.1024 0.2572 0.0000 0.2342
#> 2 0.0421 0.0902 1.4662 0.1140 0.3021 0.0444 0.0651
#> 3 0.8610 33.7458 0.6109 0.0000 0.0642 0.2690 1.3673
#> 4 0.0000 0.7154 0.6136 0.0822 0.0458 0.0961 0.0000
#> 5 0.0166 0.1599 0.1118 0.0673 2.1791 0.0262 1.2314
#> 6 0.1780 3.3316 0.1714 0.9296 0.0000 0.8857 6.3743
#> AC105046.1 LINC01929 AP001434.1 AC105219.1 AC133644.1 FAM153CP AC093515.1
#> 1 1.5372 0.7004 7.6471 0.0000 0.1563 0.0099 0.0000
#> 2 0.0940 1.7320 2.7820 0.1571 2.3483 0.1389 0.0526
#> 3 0.7133 0.3030 1.5853 0.0000 1.4043 0.0074 0.0000
#> 4 0.0135 0.2497 0.2359 0.4528 0.0000 0.1502 0.0253
#> 5 0.0222 1.1255 0.9668 0.1856 0.6166 2.6236 0.9121
#> 6 0.4428 0.0262 0.0000 0.2846 0.0000 0.0331 0.0636
#> AC004990.1 HOTAIR AC055854.1 SIRLNT
#> 1 0.9918 11.2572 0.1926 0.0000
#> 2 0.0409 9.1333 10.0963 0.0714
#> 3 0.4125 4.0364 1.1249 0.0576
#> 4 0.0295 0.1821 0.0927 0.0343
#> 5 0.0000 0.1066 1.2157 0.0000
#> 6 0.0000 1.6027 0.9711 0.0000