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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}, }
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