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inlabru vs mice

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

Shared themes:r-package

inlabru vs mice: at a glance

Featureinlabrumice
SectorInfra & APIsInfra & APIs
Velocity score2.50.0
Sparks · 30d00
Top themesbayesian-modelling, spatial-statistics, r-package, api-consolidationmissing-data, multiple-imputation, statistics, r-package
Last editorial update4h ago48m 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 mice?

mice can finally predict, not just estimate, from multiply imputed data.

mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.

Read the full mice trajectory →

inlabru vs mice: 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.

M
mice
INFRA · APIS
0.0

mice can finally predict, not just estimate, from multiply imputed data.

◆ Current state

mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.

◆ Where it's heading

Two things are happening. The imputation method catalogue keeps widening — lasso variants, multivariate PMM, categorical PMM via canonical correlation — while the pooling side is being extended past its original purpose, first to synthetic data, now to predictions on held-out sets. That second thread points at predictive modelling workflows rather than the inferential ones mice was built for. Meanwhile the maintainers keep finding consequential old bugs: the augment() ordered-factor defect in 3.18.0 had been silently degrading ordinal imputations for years.

◆ Prediction

predict_mi() is framed around evaluating predictive performance on test sets, and the ignore argument added in 3.12.0 already exists to hold out rows from the imputation model. Expect the next work to join those up into a fuller train/test story for imputed data, since the pieces are now in place but not yet connected.

Alternatives to inlabru and mice

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 mice.

See all inlabru alternatives → · See all mice alternatives →

Recent activity from inlabru and mice

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 agomicemice 3.19.0
  5. 1y agoinlabruMapper classes shortened to bm_*, experimental predictor aggregation
  6. 1y agomicemice 3.18.0
  7. 1y agomicemice 3.17.0
  8. 1y agoinlabruDrops sp and ggmap for an sf-native spatial stack
  9. 3y agomicemice 3.16.0
  10. 3y agomicemice 3.15.0
  11. 4y agomicemice 3.14.0

Frequently asked questions

What is the difference between inlabru and mice?

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 mice?

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 mice?

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