Extra Recipes Steps for Dealing with Unbalanced Data R package themis version 1.1.0 A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 <doi:10.48550/arXiv.1106.1813>, BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008. Or by decreasing the number of majority cases using NearMiss 2003 <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek link removal 1976. Software Depends: R (>= 4.1), recipes (>= 1.1.0) Imports: cli, dplyr, generics (>= 0.1.0), glue, gower, hardhat, lifecycle (>= 1.0.3), purrr, RANN, rlang (>= 1.1.0), ROSE, tibble, vctrs, withr Suggests: covr, dials (>= 1.2.0), ggplot2, kernlab, modeldata, philentropy, testthat (>= 3.0.0) Emil Hvitfeldt <emil.hvitfeldt@posit.co> Comprehensive R Archive Network (CRAN) Emil Hvitfeldt [aut, cre] (ORCID: <https://orcid.org/0000-0002-0679-1945>), Posit Software, PBC [cph, fnd] (ROR: <https://ror.org/03wc8by49>) MIT + file LICENSE (https://CRAN.R-project.org/package=themis/LICENSE) 2026-08-02 application/tgz https://CRAN.R-project.org/package=themis doi:10.32614/CRAN.package.themis