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

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