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.

An Interpretable Machine Learning-Based Automatic Clinical Score
Generator
c(person("Feng", "Xie", role = c("aut","cre"), comment = c(ORCID = "0000-0002-0215-667X"),
email= "xief@u.duke.nus.edu"),
person("Yilin", "Ning", role = c("aut"), comment = c(ORCID = "0000-0002-6758-4472"),
email= "yilin.ning@duke-nus.edu.sg"),
person("Han", "Yuan", role = c("aut"), comment = c(ORCID = "0000-0002-2674-6068"),
email= "yuan.han@u.duke.nus.edu"),
person("Ehsan", "Saffari", role = c("aut"), comment = c(ORCID = "0000-0002-6473-4375"),
email = "ehsan.saffari@duke-nus.edu.sg"),
person("Bibhas", "Chakraborty", role = c("aut"), comment = c(ORCID = "0000-0002-7366-0478"),
email = "bibhas.chakraborty@duke-nus.edu.sg"),
person("Nan", "Liu", role = c("aut"), comment = c(ORCID = "0000-0003-3610-4883"),
email = "liu.nan@duke-nus.edu.sg"))
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.
tableone, pROC, randomForest, ggplot2, knitr

c(person("Feng", "Xie", role = c("aut","cre"), comment = c(ORCID = "0000-0002-0215-667X"),
email= "xief@u.duke.nus.edu"),
person("Yilin", "Ning", role = c("aut"), comment = c(ORCID = "0000-0002-6758-4472"),
email= "yilin.ning@duke-nus.edu.sg"),
person("Han", "Yuan", role = c("aut"), comment = c(ORCID = "0000-0002-2674-6068"),
email= "yuan.han@u.duke.nus.edu"),
person("Mingxuan", "Liu", role = c("aut"), comment = c(ORCID = "0000-0002-4274-9613"),
email= "m.liu@duke-nus.edu.sg"),
person("Ehsan", "Saffari", role = c("aut"), comment = c(ORCID = "0000-0002-6473-4375"),
email = "ehsan.saffari@duke-nus.edu.sg"),
person("Bibhas", "Chakraborty", role = c("aut"), comment = c(ORCID = "0000-0002-7366-0478"),
email = "bibhas.chakraborty@duke-nus.edu.sg"),
person("Nan", "Liu", role = c("aut"), comment = c(ORCID = "0000-0003-3610-4883"),
email = "liu.nan@duke-nus.edu.sg"))
tableone, pROC, randomForest, ggplot2, rpart, knitr