If you use the SVEMnet package, please cite the following works:
Karl A (2024). SVEMnet: Self-Validated Ensemble Models with Elastic Net Regression. R package version 1.0.1.
Karl A (2024). “A randomized permutation whole-model test heuristic for Self-Validated Ensemble Models (SVEM).” Chemometrics and Intelligent Laboratory Systems, 249, 105122. doi:10.1016/j.chemolab.2024.105122.
Lemkus T, Gotwalt C, Ramsey P, Weese M (2021). “Self-validated ensemble models for design of experiments.” Chemometrics and Intelligent Laboratory Systems, 219, 104439. doi:10.1016/j.chemolab.2021.104439.
Friedman J, Tibshirani R, Hastie T (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01.
Corresponding BibTeX entries:
@Manual{,
title = {SVEMnet: Self-Validated Ensemble Models with Elastic Net
Regression},
author = {Andrew T. Karl},
year = {2024},
note = {R package version 1.0.1},
}
@Article{,
title = {A randomized permutation whole-model test heuristic for
Self-Validated Ensemble Models (SVEM)},
author = {Andrew T. Karl},
journal = {Chemometrics and Intelligent Laboratory Systems},
year = {2024},
volume = {249},
pages = {105122},
doi = {10.1016/j.chemolab.2024.105122},
keywords = {Formulation optimization Joint optimization Mixture
experiment Multiple response experiment SVEM},
}
@Article{,
title = {Self-validated ensemble models for design of experiments},
author = {Trent Lemkus and Christopher Gotwalt and Philip Ramsey
and Maria L. Weese},
journal = {Chemometrics and Intelligent Laboratory Systems},
year = {2021},
volume = {219},
pages = {104439},
doi = {10.1016/j.chemolab.2021.104439},
keywords = {Box-Behnken designs Definitive screening designs
Forward selection Fractional weighted bootstrap Lasso},
}
@Article{,
title = {Regularization Paths for Generalized Linear Models via
Coordinate Descent},
author = {Jerome Friedman and Robert Tibshirani and Trevor Hastie},
journal = {Journal of Statistical Software},
year = {2010},
volume = {33},
number = {1},
pages = {1--22},
doi = {10.18637/jss.v033.i01},
}