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lazyeval vs modelbpp

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

lazyeval vs modelbpp: at a glance

Featurelazyevalmodelbpp
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesnon-standard-evaluation, r-c-api, dormancy-revival, tidyversestructural-equation-modeling, statistics, r-package, cran
Last editorial update13m ago1h ago
WebsiteVisit →Visit →

What is lazyeval?

A package retired in 2017 just got rewritten against R's public C API.

lazyeval was the tidyverse's pre-rlang non-standard evaluation layer, formally set aside in 2017 when tidy evaluation replaced it. After eight and a half years without a release, 0.2.3 arrives as a compliance rewrite: the implementation now uses R's public C API, and the release note states it may differ from the historical one in subtle ways. Nothing about the package's role has changed.

Read the full lazyeval trajectory →

What is modelbpp?

A structural-equation model comparison package whose feed carries links, not release notes.

modelbpp computes model-implied Bayesian posterior probabilities for structural equation models, one of several R packages from the same author covering moderation, mediation and model-comparison workflows. Its release feed is not a changelog: every entry points at the package website rather than describing what changed, so the substance of each release is not visible here. Version numbering has moved steadily from 0.1.x to 0.4.0 across roughly three years.

Read the full modelbpp trajectory →

lazyeval vs modelbpp: editorial side-by-side

L
lazyeval
ANALYTICS
0.0

A package retired in 2017 just got rewritten against R's public C API.

◆ Current state

lazyeval was the tidyverse's pre-rlang non-standard evaluation layer, formally set aside in 2017 when tidy evaluation replaced it. After eight and a half years without a release, 0.2.3 arrives as a compliance rewrite: the implementation now uses R's public C API, and the release note states it may differ from the historical one in subtle ways. Nothing about the package's role has changed.

◆ Where it's heading

This is a dormancy revival driven entirely from outside, R core tightening what counts as the public C API forces packages using older internals to be rewritten or be archived. lazyeval is still a dependency deep in older package trees, so keeping it installable matters more than developing it. The caveat about subtle behavioural differences is the notable part: a package nobody is developing has changed behaviour in ways its release note declines to enumerate.

◆ Prediction

Expect no further development, only additional compliance releases if R core tightens the C API again.

M
modelbpp
ANALYTICS
2.5

A structural-equation model comparison package whose feed carries links, not release notes.

◆ Current state

modelbpp computes model-implied Bayesian posterior probabilities for structural equation models, one of several R packages from the same author covering moderation, mediation and model-comparison workflows. Its release feed is not a changelog: every entry points at the package website rather than describing what changed, so the substance of each release is not visible here. Version numbering has moved steadily from 0.1.x to 0.4.0 across roughly three years.

◆ Where it's heading

What can be read from this feed is cadence rather than content — releases clustered noticeably more tightly through 2026 than in the preceding two years, with three in five months against two in the prior eighteen. Because the entries carry no detail, any statement about what is being built would be speculation. The pattern of a stable CRAN package accelerating its release rate is the only reliable signal available.

◆ Prediction

The feed does not describe its changes, so the direction of development cannot be read from these entries; the accelerating 2026 cadence is the only thing it supports.

Alternatives to lazyeval and modelbpp

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 lazyeval or modelbpp.

See all lazyeval alternatives → · See all modelbpp alternatives →

Recent activity from lazyeval and modelbpp

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

  1. 29d agomodelbppCRAN Release 0.4.0
  2. 3mo agomodelbppCRAN Release 0.3.0
  3. 4mo agolazyevalReimplemented against R's public C API after eight years dormant
  4. 5mo agomodelbppCRAN Release 0.2.0
  5. 2y agomodelbppCRAN Release 0.1.3
  6. 2y agomodelbppCRAN Release 0.1.2
  7. 8y agolazyevalDevelopment ends as the tidyverse moves to tidy evaluation
  8. 10y agolazyevalFormula-based lazy evaluation system introduced
  9. 11y agolazyevallazyeval 0.1.10
  10. 11y agolazyevallazyeval 0.1.9

Frequently asked questions

What is the difference between lazyeval and modelbpp?

They serve adjacent needs but don't currently overlap on shipped themes. modelbpp 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 lazyeval better than modelbpp?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. modelbpp 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 lazyeval?

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

What are the best alternatives to modelbpp?

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