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poissonreg vs sparsevctrs

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

Shared themes:tidymodels

poissonreg vs sparsevctrs: at a glance

Featurepoissonregsparsevctrs
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themestidymodels, count-regression, glmnet, r-languagesparse-data, tidymodels, altrep, numerical-computing
Last editorial update1h ago50m ago
WebsiteVisit →Visit →

What is poissonreg?

poissonreg gave its models away to parsnip and kept the glue — now it just keeps glmnet honest.

poissonreg is a tidymodels extension that wires Poisson and zero-inflated count regression into the parsnip interface. Its defining event was giving up ownership: the model definition functions moved into parsnip itself, leaving this package as engine bindings and prediction plumbing. The current dev release is entirely correctness and hygiene work — glmnet predictions now default to mean counts rather than the linear predictor, and single-observation prediction works at last.

Read the full poissonreg trajectory →

What is sparsevctrs?

Sparse vectors stopped being a storage trick and became something you can do arithmetic on

sparsevctrs supplies sparse vectors that live inside ordinary data frames and tibbles, which is what lets tidymodels carry wide, mostly-zero feature matrices without densifying them. Through 0.2.0 and 0.3.0 the package built out a computation layer on top of that storage — first summary statistics, then scalar and element-wise arithmetic — and everything since has been correctness work at the C level.

Read the full sparsevctrs trajectory →

poissonreg vs sparsevctrs: editorial side-by-side

P
poissonreg
ANALYTICS
0.0

poissonreg gave its models away to parsnip and kept the glue — now it just keeps glmnet honest.

◆ Current state

poissonreg is a tidymodels extension that wires Poisson and zero-inflated count regression into the parsnip interface. Its defining event was giving up ownership: the model definition functions moved into parsnip itself, leaving this package as engine bindings and prediction plumbing. The current dev release is entirely correctness and hygiene work — glmnet predictions now default to mean counts rather than the linear predictor, and single-observation prediction works at last.

◆ Where it's heading

Release cadence has collapsed from yearly to a four-year gap between 1.0.1 and the current development version, and the content has shifted from features to deduplication against parsnip — copied helper functions replaced by the upstream originals, obsolete generic registrations removed, tests migrated to the shared extension-package pattern. This is what a stabilized tidymodels satellite looks like: the interface lives upstream, and the package's job is to not drift from it.

◆ Prediction

The dev version's accumulated fixes point to a CRAN release of 1.0.2 as the next move, with content limited to the glmnet prediction corrections rather than any new engine or model type.

S
sparsevctrs
ANALYTICS
0.0

Sparse vectors stopped being a storage trick and became something you can do arithmetic on

◆ Current state

sparsevctrs supplies sparse vectors that live inside ordinary data frames and tibbles, which is what lets tidymodels carry wide, mostly-zero feature matrices without densifying them. Through 0.2.0 and 0.3.0 the package built out a computation layer on top of that storage — first summary statistics, then scalar and element-wise arithmetic — and everything since has been correctness work at the C level.

◆ Where it's heading

The release pattern splits cleanly at 0.3.0. Before it, new functions arrive in batches; after it, five consecutive releases are bug fixes, and the bugs are the kind that come with hand-written sparse kernels: a stack imbalance when sparse_multiplication() returns all zeros, undefined behaviour in multiplication, type errors in sparse_is_na(), coercion failures on NA input. That is the expected cost of an ALTREP-backed numerical layer, and the fixes are landing steadily.

◆ Prediction

With the arithmetic surface in place and the recent releases all narrow fixes, the next one is more likely another correctness patch than a new function family. The R devel fix in 0.3.5 suggests upcoming R releases are the current source of breakage.

Alternatives to poissonreg and sparsevctrs

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 poissonreg or sparsevctrs.

See all poissonreg alternatives → · See all sparsevctrs alternatives →

Recent activity from poissonreg and sparsevctrs

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

  1. 3mo agopoissonregglmnet predictions now default to mean counts
  2. 8mo agosparsevctrsSparse character vector fix for R devel
  3. 1y agosparsevctrsStack imbalance in sparse multiplication fixed
  4. 1y agosparsevctrsSparse matrix coercion no longer errors on NA input
  5. 1y agosparsevctrssparsity() fixed for classed numeric vectors
  6. 1y agosparsevctrsUndefined behaviour in sparse multiplication fixed
  7. 1y agosparsevctrsScalar and element-wise arithmetic for sparse vectors
  8. 3y agopoissonregDocumentation regenerated for valid HTML5
  9. 4y agopoissonregCase weight support tracks parsnip 1.0.0
  10. 4y agopoissonregModel definitions move out of poissonreg into parsnip
  11. 4y agopoissonregglm becomes the default engine; tidy() for hurdle models
  12. 5y agopoissonregFirst release, with a glmnet column-order safeguard

Frequently asked questions

What is the difference between poissonreg and sparsevctrs?

Both compete on the same themes — tidymodels — within Analytics. poissonreg and sparsevctrs 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 poissonreg better than sparsevctrs?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. poissonreg and sparsevctrs 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 poissonreg?

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

What are the best alternatives to sparsevctrs?

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