← Back to home
Comparison · Infra & APIs

estimatr vs sps

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

Shared themes:r-package

estimatr vs sps: at a glance

Featureestimatrsps
SectorInfra & APIsInfra & APIs
Velocity score0.02.5
Sparks · 30d00
Top themescausal-inference, experiments, robust-standard-errors, econometricsr-package, survey-sampling, sequential-poisson, performance
Last editorial update33m ago3h ago
WebsiteVisit →Visit →

What is estimatr?

Fast design-based estimators for experiments, coasting on CRAN patches.

estimatr provides the design-based regression estimators the DeclareDesign ecosystem is built on — robust and cluster-robust standard errors, blocked and clustered randomization inference — implemented for speed rather than generality. The last three releases carry no substantive notes: each is a merge commit for a CRAN patch, one of them accompanied by a typo fix.

Read the full estimatr trajectory →

What is sps?

sps keeps sanding down sequential Poisson sampling rather than adding to it.

The package implements sequential Poisson sampling for survey design, covering inclusion probabilities, proportional allocation, replicate weights, and take-all strata. Recent releases are small and tightly scoped: a divisor method helper, an iterator that draws a sample one unit at a time, automatic selection of the replicate-weight parameter, and repeated performance work on inclusion probability calculation.

Read the full sps trajectory →

estimatr vs sps: editorial side-by-side

E
estimatr
INFRA · APIS
0.0

Fast design-based estimators for experiments, coasting on CRAN patches.

◆ Current state

estimatr provides the design-based regression estimators the DeclareDesign ecosystem is built on — robust and cluster-robust standard errors, blocked and clustered randomization inference — implemented for speed rather than generality. The last three releases carry no substantive notes: each is a merge commit for a CRAN patch, one of them accompanied by a typo fix.

◆ Where it's heading

Direction cannot be read from this feed. The release notes are unedited merge-commit messages, so the only signal is cadence — roughly annual, each release framed as a CRAN patch rather than as feature work. That pattern is consistent with a package whose estimators are considered finished and which now moves only when CRAN policy requires it.

◆ Prediction

On the evidence here the next release is another CRAN compliance patch, but the notes are too thin to support a confident read of what the maintainers are actually working on.

S
sps
INFRA · APIS
2.5

sps keeps sanding down sequential Poisson sampling rather than adding to it.

◆ Current state

The package implements sequential Poisson sampling for survey design, covering inclusion probabilities, proportional allocation, replicate weights, and take-all strata. Recent releases are small and tightly scoped: a divisor method helper, an iterator that draws a sample one unit at a time, automatic selection of the replicate-weight parameter, and repeated performance work on inclusion probability calculation.

◆ Where it's heading

Development is consolidation rather than expansion. Most releases either speed up an existing routine or remove a decision the user previously had to make by hand, such as picking the smallest parameter that keeps replicate weights non-negative. Documentation and tooling get comparable attention to the algorithms, with a dedicated vignette on inclusion probabilities and a recent switch of test and documentation infrastructure. The API surface has been essentially stable across the window.

◆ Prediction

Expect continued small ergonomic and performance releases against the existing function set rather than new sampling designs, which is the pattern every release in this window follows.

Alternatives to estimatr and sps

Other Infra & APIs 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 estimatr or sps.

See all estimatr alternatives → · See all sps alternatives →

Recent activity from estimatr and sps

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

  1. 1mo agospsDocumentation polish; switches to tinytest and litedown
  2. 9mo agospsFixes extra argument handling in sps_iterator()
  3. 11mo agospsAdds divisor_method() and a one-unit-at-a-time sampling iterator
  4. 1y agospsAdds an inclusion-probability vignette and faster partial sorting
  5. 1y agospsAutomatic tau selection for replicate weights
  6. 1y agoestimatrCRAN version 1.0.4
  7. 2y agoestimatrCRAN version 1.0.2
  8. 2y agospsAdds becomes_ta() for take-all stratum sample sizes
  9. 3y agoestimatrCRAN version 1.0.0

Frequently asked questions

What is the difference between estimatr and sps?

Both compete on the same themes — r-package — within Infra & APIs. sps 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 estimatr better than sps?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. sps 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 Infra & APIs products to evaluate alongside.

What are the best alternatives to estimatr?

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

What are the best alternatives to sps?

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