Construct Explainable Nomogram for a Machine Learning Model.

rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms

rmlnomogram is an R package designed to construct an explainable nomogram for a machine learning (ML) model to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. This package enables a nomogram creation for any ML prediction models, which is conventionally limited to only a linear/logistic regression model. This nomogram may indicate the explainability value per feature, e.g., the Shapley additive explanation value, for each individual. However, this package only allows a nomogram creation for a model using categorical without or with single numerical predictors.

Features

• Construct nomogram from machine learning algorithms: Construct a nomogram for either binary or continuous outcomes based on provided sample features/predictors and outputs.

• Incorporate model explainability: We can also incorporate feature/predictor explainability values, such as SHAP values, for a nomogram with probability.

Installation

You can install rmlnomogram from CRAN with:

install.packages("rmlnomogram")

You can install the development version of rmlnomogram from GitHub with:

# install.packages("devtools")
devtools::install_github("herdiantrisufriyana/rmlnomogram")

Quick Start

Load necessary packages.

library(rmlnomogram)

Nomogram for categorical predictors and binary outcome without probability.

data(nomogram_features)
data(nomogram_outputs)
create_nomogram(nomogram_features, nomogram_outputs)

Nomogram for categorical predictors and binary outcome with probability.

create_nomogram(nomogram_features, nomogram_outputs, prob = TRUE)

Add model explainability.

data(nomogram_shaps)
create_nomogram(nomogram_features, nomogram_outputs, nomogram_shaps, prob = TRUE)

Nomogram for categorical and 1 numerical predictors and binary outcome with probability.

data(nomogram_features2)
data(nomogram_outputs2)
create_nomogram(nomogram_features2, nomogram_outputs2, prob = TRUE)

Add model explainability.

data(nomogram_shaps2)
create_nomogram(nomogram_features2, nomogram_outputs2, nomogram_shaps2, prob = TRUE)

Nomogram for categorical predictors and continuous outcome.

data(nomogram_features3)
data(nomogram_outputs3)
create_nomogram(nomogram_features3, nomogram_outputs3, est = TRUE)

Add model explainability.

data(nomogram_shaps3)
create_nomogram(nomogram_features3, nomogram_outputs3, nomogram_shaps3, est = TRUE)

Nomogram for categorical and 1 numerical predictors and continuous outcome

data(nomogram_features4)
data(nomogram_outputs4)
create_nomogram(nomogram_features4, nomogram_outputs4, est = TRUE)

Add model explainability.

data(nomogram_shaps4)
create_nomogram(nomogram_features4, nomogram_outputs4, nomogram_shaps4, est = TRUE)

Vignettes

Explore detailed examples and methodologies in the following vignettes:

• Machine Learning Nomogram Exemplar: A guide through basic scenarios demonstrating how to use rmlnomogram to create a nomogram.

• Reference Manual: Comprehensive documentation of all functions and features available in rmlnomogram. Ideal for detailed reference and advanced use cases.

License

rmlnomogram is licensed under the MIT license. See the LICENSE file for more details.

Citation

If you use rmlnomogram in your research, please consider citing it:

@misc{rmlnomogram2025,
  author = {Herdiantri Sufriyana and Emily Chia-Yu Su},
  title = {rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\\url{https://github.com/herdiantrisufriyana/rmlnomogram}}
}

Contact

For questions or support, please contact herdi[at]tmu.edu.tw.

Programming Environment

System requirements

Install Docker desktop once in your machine. Start the service every time you build this project image or run the container.

Installation guide

Build the project image once for a new machine (currently support AMD64 and ARM64).

docker build -t rmlnomogram --load .

Run the container every time you start working on the project. Change left-side port numbers for either Rstudio or Jupyter lab if any of them is already used by other applications.

In terminal:

docker run -d -p 8787:8787 -p 8888:8888 -v "$(pwd)":/home/rstudio/project --name rmlnomogram_container rmlnomogram

In command prompt:

docker run -d -p 8787:8787 -p 8888:8888 -v "%cd%":/home/rstudio/project --name rmlnomogram_container rmlnomogram

Instructions for use

Rstudio

Change port number in the link, accordingly, if it is already used by other applications.

Visit http://localhost:8787. Username: rstudio Password: 1234

Your working directory is ~/project.

Jupyter lab

Use terminal/command prompt to run the container terminal.

docker exec -it rmlnomogram_container bash

In the container terminal, run jupyter lab using this line of codes.

jupyter-lab --ip=0.0.0.0 --no-browser --allow-root

Click a link in the results to open jupyter lab in a browser. Change port number in the link, accordingly, if it is already used by other applications.