dCVnet
an R package for double nested cross-validation of regularised logistic models
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A software tool “dCVnet”, developed by Dr Andrew Lawrence, is an R wrapper for the glmnet package to implement regularized logistic regression with double (nested) cross-validation for internal validation and made this easy-to-use tool available for use by the scientific and clinical community as an R package.
Package link: https://www.github.com/AndrewLawrence/dCVnet
Corresponding paper: https://pubmed.ncbi.nlm.nih.gov/34175478/ Lawrence, A. J., Stahl, D., Duan, S., Fennema, D., Jaeckle, T., Young, A. H., ... & Zahn, R. (2022). Neurocognitive measures of self-blame and risk prediction models of recurrence in major depressive disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 7(3), 256-264.
Video explaining the package: Introduction to dCVnet – software for clinical prediction
Further details: In contrast to traditional statistical methods, regularized regression allows the analyses of a large number of predictors relative to sample size. Regularization provides a means to reduce overfitting by constraining the magnitude of the regression coefficients through the introduction of a penalty. DCVnet provides a documented and standardized implementation of this particular machine learning pipeline, making it accessible to researchers lacking the programming experience required for more general machine learning software environments.
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