Please cite Karl (2026) as the primary reference for SVEMnet's core workflow and the package manual to identify the software version used. The journal articles are the fixed versions of record. The authors occasionally update the corresponding arXiv manuscripts.

Karl A (2026). “SVEMnet: An R package for self-validated elastic-net ensembles and multi-response optimization in small-sample mixture-process experiments.” Chemometrics and Intelligent Laboratory Systems, 271, 105660. doi:10.1016/j.chemolab.2026.105660. https://arxiv.org/abs/2511.20968.

Karl A, Essex S, Wisnowski J, Rushing H (2023). “A workflow for lipid nanoparticle (LNP) formulation optimization using designed mixture-process experiments and self-validated ensemble models (SVEM).” Journal of Visualized Experiments, e65200. doi:10.3791/65200. https://arxiv.org/abs/2212.11264.

Karl A (2026). SVEMnet: Self-Validated Ensemble Models with Lasso and Relaxed Elastic Net Regression. R package version 4.0.0.

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:

  @Article{,
    title = {SVEMnet: An R package for self-validated elastic-net
      ensembles and multi-response optimization in small-sample
      mixture-process experiments},
    author = {Andrew T. Karl},
    journal = {Chemometrics and Intelligent Laboratory Systems},
    year = {2026},
    volume = {271},
    pages = {105660},
    doi = {10.1016/j.chemolab.2026.105660},
    url = {https://arxiv.org/abs/2511.20968},
  }

  @Article{,
    title = {A workflow for lipid nanoparticle (LNP) formulation
      optimization using designed mixture-process experiments and
      self-validated ensemble models (SVEM)},
    author = {Andrew T. Karl and Sean Essex and James Wisnowski and
      Heath Rushing},
    journal = {Journal of Visualized Experiments},
    year = {2023},
    number = {198},
    pages = {e65200},
    doi = {10.3791/65200},
    url = {https://arxiv.org/abs/2212.11264},
  }

  @Manual{,
    title = {SVEMnet: Self-Validated Ensemble Models with Lasso and
      Relaxed Elastic Net Regression},
    author = {Andrew T. Karl},
    year = {2026},
    note = {R package version 4.0.0},
  }

  @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},
  }