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Comparison · Infra & APIs

inlabru vs PINstimation

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

Shared themes:r-package

inlabru vs PINstimation: at a glance

FeatureinlabruPINstimation
SectorInfra & APIsInfra & APIs
Velocity score2.50.0
Sparks · 30d00
Top themesbayesian-modelling, spatial-statistics, r-package, api-consolidationmarket-microstructure, finance, informed-trading, r-package
Last editorial update5h ago1h ago
WebsiteVisit →Visit →

What is inlabru?

A Bayesian spatial modelling package rebuilding its foundations one breaking release at a time

inlabru wraps INLA for spatial, point-process and latent-Gaussian models in R. It is mid-modernisation: since 2.12.0 cut the sp stack, each release has renamed part of the public surface, standardised how external packages attach custom mappers, or replaced internal machinery. 2.15.0 is the latest step, pairing a new predictor evaluation and linearisation implementation with broom's tidy(), glance() and augment() methods and four non-zero-truncated observation families.

Read the full inlabru trajectory →

What is PINstimation?

A market-microstructure toolkit that keeps adding estimators as the papers land.

PINstimation estimates probability-of-informed-trading models — PIN, multilayer PIN, adjusted PIN and VPIN — from trade and quote data, and handles the trade classification and aggregation that feeds them. The current 0.2.0 adds ivpin(), a maximum-likelihood variant of VPIN from Ke and Lin (2017). The package's early history is compressed into a single hour of backfilled tags in October 2022, so version order there does not track release order.

Read the full PINstimation trajectory →

inlabru vs PINstimation: editorial side-by-side

I
inlabru
INFRA · APIS
2.5

A Bayesian spatial modelling package rebuilding its foundations one breaking release at a time

◆ Current state

inlabru wraps INLA for spatial, point-process and latent-Gaussian models in R. It is mid-modernisation: since 2.12.0 cut the sp stack, each release has renamed part of the public surface, standardised how external packages attach custom mappers, or replaced internal machinery. 2.15.0 is the latest step, pairing a new predictor evaluation and linearisation implementation with broom's tidy(), glance() and augment() methods and four non-zero-truncated observation families.

◆ Where it's heading

The arc is consolidation of the extension surface rather than expansion of the model catalogue. Every release adds mappers or families with one hand and removes a dependency, a re-export or a deprecated path with the other — plyr in 2.15.0, fmesher's Depends entry in 2.14.1, sp and ggmap in 2.12.0. The compatibility flag bru_compat_pre_2_14_enable and the temporary fm_int/fm_pixels re-exports show a maintainer sequencing breaks across releases instead of landing them together.

◆ Prediction

The 2.14 compatibility flag is still defaulting to TRUE and the fmesher re-exports are described in the entries as temporary, so the next obvious move is a release that flips bru_compat_pre_2_14_enable off and drops those re-exports.

P
PINstimation
INFRA · APIS
0.0

A market-microstructure toolkit that keeps adding estimators as the papers land.

◆ Current state

PINstimation estimates probability-of-informed-trading models — PIN, multilayer PIN, adjusted PIN and VPIN — from trade and quote data, and handles the trade classification and aggregation that feeds them. The current 0.2.0 adds ivpin(), a maximum-likelihood variant of VPIN from Ke and Lin (2017). The package's early history is compressed into a single hour of backfilled tags in October 2022, so version order there does not track release order.

◆ Where it's heading

Each release tracks the literature: a Bayesian PIN estimator from Griffin et al., an improved VPIN from Ke and Lin, initial-parameter generation realigned to Ersan and Ghachem. The other steady thread is data handling — matrix inputs so the estimators compose with rolling windows, user-specified aggregation frequencies, and now quote leads as well as lags. The three-year gap between 0.1.2 and 0.2.0 makes this a slow, publication-paced package rather than an actively developed one.

◆ Prediction

On this pattern the next release adds whatever estimator the authors publish next, since two of the three feature releases here implement a specific paper. Nothing in the entries points to a change in the package's structure.

Alternatives to inlabru and PINstimation

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 inlabru or PINstimation.

See all inlabru alternatives → · See all PINstimation alternatives →

Recent activity from inlabru and PINstimation

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

  1. 23d agoinlabruPredictor linearisation rewritten; broom tidiers, truncated families
  2. 3mo agoinlabruBugfix release: factor contrasts, raster extraction, error classes
  3. 5mo agoinlabruNew mappers, standardised cgeneric support, bru_obs storage refactor
  4. 8mo agoPINstimationPINstimation 0.2.0
  5. 1y agoinlabruMapper classes shortened to bm_*, experimental predictor aggregation
  6. 1y agoinlabruDrops sp and ggmap for an sf-native spatial stack
  7. 3y agoPINstimationPINstimation v0.1.2
  8. 3y agoPINstimationPINstimation v0.1.1
  9. 3y agoPINstimationPINstimation v0.0.1-beta
  10. 3y agoPINstimationPINstimation v0.1.0

Frequently asked questions

What is the difference between inlabru and PINstimation?

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

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

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

What are the best alternatives to PINstimation?

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