Description: General-to-Specific (GETS) modelling of the mean and variance of a regression. NOTE: The package has been succeeded by gets, also available on the CRAN, which is more user-friendly, faster and easier to extend. Users are therefore encouraged to consider gets instead.

Title: An Interpretable Machine Learning-Based Automatic Clinical Score
Authors@R: c(person("Feng", "Xie", role = c("aut","cre"), comment = c(ORCID = "0000-0002-0215-667X"),
person("Ehsan", "Saffari", role = c("aut"), comment = c(ORCID = "0000-0002-6473-4375"),
person("Bibhas", "Chakraborty", role = c("aut"), comment = c(ORCID = "0000-0002-7366-0478"),
Description: A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper<doi:10.2196/21798>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.
Imports: tableone, pROC, randomForest, ggplot2, knitr
Author: Feng Xie [aut, cre] (<https://orcid.org/0000-0002-0215-667X>),

Imports: tableone, pROC, randomForest, ggplot2, rpart, knitr

person("Seyed Ehsan", "Saffari", role = c("aut"), comment = c(ORCID = "0000-0002-6473-4375"),
Description: A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The The original AutoScore structure is described in the research paper<doi:10.2196/21798>. A full tutorial can be found here<https://nliulab.github.io/AutoScore/>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.
Imports: tableone, pROC, randomForest, ggplot2, knitr, Hmisc, car,
coxed, dplyr, ordinal, survival, tidyr, plotly, magrittr,
Seyed Ehsan Saffari [aut] (<https://orcid.org/0000-0002-6473-4375>),