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discrim vs sparklyr

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

discrim vs sparklyr: at a glance

Featurediscrimsparklyr
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themestidymodels, discriminant analysis, parsnip extension, classificationspark, databricks, dbplyr-compatibility, maintenance
Last editorial update54m ago1h 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 sparklyr?

sparklyr now spends its releases absorbing dbplyr changes and feeding pysparklyr

sparklyr connects R to Spark, and almost nothing in this window originates inside the package. Releases restore compatibility after dbplyr changes its SQL generation, adapt to Spark 4.0 and to R 4.4's version-comparison changes, and convert functions into S3 methods so pysparklyr can supply its own implementations.

Read the full sparklyr trajectory →

discrim vs sparklyr: 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.

S
sparklyr
ANALYTICS
0.0

sparklyr now spends its releases absorbing dbplyr changes and feeding pysparklyr

◆ Current state

sparklyr connects R to Spark, and almost nothing in this window originates inside the package. Releases restore compatibility after dbplyr changes its SQL generation, adapt to Spark 4.0 and to R 4.4's version-comparison changes, and convert functions into S3 methods so pysparklyr can supply its own implementations.

◆ Where it's heading

Two dependencies set the agenda. dbplyr repeatedly changes identifier quoting and lazy-table internals, and each change costs sparklyr a release. Meanwhile the package is being hollowed into a backend: ml_fit(), spark_apply(), spark_write_delta() and now tune_grid_spark() exist as methods so that pysparklyr, the Databricks Connect path, can override them. Dependency removal - tibble, rappdirs, digest - runs alongside as the package slims down.

◆ Prediction

Expect the next releases to continue tracking dbplyr and Spark versions, and more functions to be converted to methods as functionality shifts toward pysparklyr; new capability arriving in sparklyr itself looks unlikely.

Alternatives to discrim and sparklyr

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 sparklyr.

See all discrim alternatives → · See all sparklyr alternatives →

Recent activity from discrim and sparklyr

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

  1. 1mo agosparklyrRestores compatibility after dbplyr changed Hive quoting
  2. 3mo agosparklyrAdds tune_grid_spark() for pysparklyr to implement
  3. 8mo agodiscrimFix for FDA models failing at prediction time
  4. 10mo agosparklyrFixes lazy-table field lookup and a name collision
  5. 11mo agodiscrimDocumentation links updated to stay on CRAN
  6. 1y agosparklyrCatches up with released Spark 4.0; ml_load() reads via Spark
  7. 2y agosparklyrDatabricks autoloader streaming ingestion; R 4.4 fixes
  8. 2y agosparklyrDrops tibble and rappdirs; retires Spark 2.3 JARs
  9. 4y agodiscrimCase weights enabled for flexible and linear discriminant models
  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 sparklyr?

They serve adjacent needs but don't currently overlap on shipped themes. discrim and sparklyr are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is discrim better than sparklyr?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. discrim and sparklyr are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). 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 sparklyr?

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