rmlnomogram: Construct Explainable Nomogram for a Machine Learning Model
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. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rmlnomogram/blob/master/doc/ml_nomogram_exemplar.html>.
| Version: |
0.1.2 |
| Depends: |
R (≥ 4.4) |
| Imports: |
dplyr, purrr, broom, stats, ggplot2, ggpubr, stringr, tidyr, utils |
| Suggests: |
tidyverse, knitr, caret, randomForest, iml, testthat (≥
3.0.0) |
| Published: |
2025-01-08 |
| DOI: |
10.32614/CRAN.package.rmlnomogram |
| Author: |
Herdiantri Sufriyana
 [aut, cre],
Emily Chia-Yu Su
[aut] |
| Maintainer: |
Herdiantri Sufriyana <herdi at nycu.edu.tw> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
rmlnomogram results |
Documentation:
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