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discrim vs themis

A side-by-side editorial comparison of discrim and themis — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:tidymodels

discrim vs themis: at a glance

Featurediscrimthemis
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themestidymodels, discriminant analysis, parsnip extension, classificationr, tidymodels, class-imbalance, resampling
Last editorial update53m ago3h ago
WebsiteVisit →Visit →

What is discrim?

discrim settled into a thin engine shim after handing its model definitions to parsnip.

discrim is the parsnip extension for discriminant analysis, exposing linear, quadratic, flexible and regularized variants through the tidymodels interface. Its model definition functions moved into parsnip itself in 0.2.0, leaving this package as the engine and prediction layer. The two most recent releases are a documentation fix and a single prediction bug.

Read the full discrim trajectory →

What is themis?

themis is back to adding real resampling algorithms after a documentation-heavy stretch.

themis supplies recipes steps for handling class imbalance in tidymodels. The 1.0.x line was consumed by documentation accuracy, message translation and internal consistency work. Version 1.1.0 returns to substance with two new under-sampling methods.

Read the full themis trajectory →

discrim vs themis: editorial side-by-side

D
discrim
ANALYTICS
0.0

discrim settled into a thin engine shim after handing its model definitions to parsnip.

◆ Current state

discrim is the parsnip extension for discriminant analysis, exposing linear, quadratic, flexible and regularized variants through the tidymodels interface. Its model definition functions moved into parsnip itself in 0.2.0, leaving this package as the engine and prediction layer. The two most recent releases are a documentation fix and a single prediction bug.

◆ Where it's heading

The package built out method coverage early, adding quadratic discriminant analysis in 0.1.2, the sda and sparsediscrim engines in 0.1.3 and case weights in 1.0.0, then stopped growing. Handing definitions upstream to parsnip in 0.2.0 confirmed the shape: discrim is where engines are wired, not where the API lives. Cadence since 2022 is roughly one small fix a year.

◆ Prediction

Nothing in the entries points to new methods or engines; the next release is most likely another CRAN or prediction-path fix.

T
themis
ANALYTICS
2.5

themis is back to adding real resampling algorithms after a documentation-heavy stretch.

◆ Current state

themis supplies recipes steps for handling class imbalance in tidymodels. The 1.0.x line was consumed by documentation accuracy, message translation and internal consistency work. Version 1.1.0 returns to substance with two new under-sampling methods.

◆ Where it's heading

The package grows by adding algorithms rather than restructuring itself. tomek() was rewritten to handle multiple classes and drop the unbalanced dependency, case weights arrived at 1.0.0, and cluster-centroid and condensed-nearest-neighbour under-sampling arrive now — each shipped as both a recipes step and a direct-implementation function.

◆ Prediction

Expect further under- and over-sampling methods in the same paired form, as the package fills out coverage of the standard class-imbalance literature.

Alternatives to discrim and themis

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either discrim or themis.

See all discrim alternatives → · See all themis alternatives →

Recent activity from discrim and themis

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 10d agothemisthemis 1.1.0 adds cluster-centroid and CNN under-sampling
  2. 8mo agodiscrimFix for FDA models failing at prediction time
  3. 11mo agodiscrimDocumentation links updated to stay on CRAN
  4. 1y agothemisthemis 1.0.3 corrects resampling direction in documentation
  5. 2y agothemisthemis 1.0.2 makes internal consistency and speed changes
  6. 3y agothemisthemis 1.0.1 fixes upsampling errors when none is needed
  7. 4y agothemisthemis 1.0.0 adds case weights to up- and down-sampling
  8. 4y agodiscrimCase weights enabled for flexible and linear discriminant models
  9. 4y agothemisthemis 0.2.2 rewrites tomek() for multiclass, drops a dependency
  10. 4y agodiscrimModel definitions moved upstream into parsnip
  11. 5y agodiscrimsda and sparsediscrim engines added for LDA and QDA
  12. 5y agodiscrimdiscrim_quad() added; package relicensed to MIT

Frequently asked questions

What is the difference between discrim and themis?

Both compete on the same themes — tidymodels — within Analytics. themis is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is discrim better than themis?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. themis is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to discrim?

Top discrim alternatives in Analytics are ranked by recent ship velocity. Browse the "discrim alternatives" section above for the current picks, or visit /alternatives/discrim for the full list with editorial commentary on each.

What are the best alternatives to themis?

Top themis alternatives in Analytics are ranked by recent ship velocity. Browse the "themis alternatives" section above for the current picks, or visit /alternatives/themis for the full list with editorial commentary on each.