# 00. Installation & Setup

## Welcome to SuperSurv!

`SuperSurv` is designed to be a unified ecosystem for machine learning and survival analysis. The framework integrates modern ensemble learning techniques for right-censored data and is motivated by recent advances in machine learning-based survival curve estimation (Westling et al., 2024) and unified ensemble modeling for survival analysis (Lyu et al., 2026).

However, installing 19 different machine learning engines at once can take a long time and cause dependency conflicts on some operating systems.

To make your experience as smooth as possible, `SuperSurv` uses a Modular Dependency Philosophy.

The core package is incredibly lightweight and installs in seconds. Heavy machine learning libraries (like XGBoost or Elastic Net) are only required when you explicitly ask to use them!

## Step 1: Install the Core Package

You can install the development version of `SuperSurv` directly from GitHub using the `devtools` or `remotes` package:

    # Install devtools if you don't have it
    if (!requireNamespace("devtools", quietly = TRUE)) {
      install.packages("devtools")
    }

    # Install SuperSurv from GitHub
    devtools::install_github("yuelyu21/SuperSurv")

Once installed, you can load the package and view all the available modeling and screening wrappers:

    library(SuperSurv)

    # See all 19 prediction models and 6 screening algorithms!
    list_wrappers()

## Step 2: Install Base Learners (Optional but Recommended)

`SuperSurv` relies on external CRAN packages to run its various base learners. If you try to run `surv.rfsrc` without having the `randomForestSRC` package installed, `SuperSurv` will gently pause and remind you to install it.

If you want to unlock the full power of the package right now, you can copy and paste the following script to install the most commonly used machine learning and interpretability engines:

    # List of highly recommended modeling engines
    ml_packages <- c(
      "survival",        # Classical Cox models
      "randomForestSRC", # Random Survival Forests
      "ranger",          # Fast Random Forests
      "xgboost",         # Extreme Gradient Boosting
      "glmnet",          # Elastic Net & Penalized Regression
      "rpart",           # Decision Trees
      "survex",          # Time-Dependent XAI (Interpretability)
      "fastshap"         # Kernel SHAP support
    )

    # Identify which ones you are missing
    missing_pkgs <- ml_packages[!(ml_packages %in% installed.packages()[,"Package"])]

    # Install the missing ones
    if(length(missing_pkgs)) install.packages(missing_pkgs)

### Specialized Packages

A few wrappers require specialized packages that you might only need for niche use cases: * `surv.svm`: Requires `survivalsvm` * `surv.gam`: Requires `mgcv` * `surv.coxboost`: Requires `CoxBoost`

## Step 3: You’re Ready!

Your environment is now completely set up. You are ready to build your first optimal survival ensemble!

👉 Click here to proceed to Tutorial 1: The SuperSurv Ensemble

## References

Westling, T., Luedtke, A., Gilbert, P. B., & Carone, M. (2024).
Inference for treatment-specific survival curves using machine learning.
Journal of the American Statistical Association.
doi:10.1080/01621459.2023.2205060

Lyu, Y., Huang, X., Lin, S. H., & Li, Z. (2026).
SuperSurv: A Unified Framework for Machine Learning Ensembles in Survival Analysis.
bioRxiv.
doi:10.64898/2026.03.11.711010