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fio vs mice

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

Shared themes:r-package

fio vs mice: at a glance

Featurefiomice
SectorInfra & APIsInfra & APIs
Velocity score0.00.0
Sparks · 30d00
Top themesinput-output-analysis, regional-economics, rust-backend, breaking-changesmissing-data, multiple-imputation, statistics, r-package
Last editorial update6h ago1h ago
WebsiteVisit →Visit →

What is fio?

Input-output economics in R with a Rust core, now spanning multiple regions.

fio builds and analyses input-output models in R, using an R6 object for the model and Rust with the faer crate for the linear algebra behind technical coefficients and the Leontief inverse. Version 1.0.0 extended it from single-region tables to multi-regional models with spillover analysis, and 1.1.0 immediately corrected the naming and measures that release introduced, renaming shock-origin columns that had been labelled as destinations and replacing an interdependence index with spillover balance and export share.

Read the full fio 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 →

fio vs mice: editorial side-by-side

F
fio
INFRA · APIS
0.0

Input-output economics in R with a Rust core, now spanning multiple regions.

◆ Current state

fio builds and analyses input-output models in R, using an R6 object for the model and Rust with the faer crate for the linear algebra behind technical coefficients and the Leontief inverse. Version 1.0.0 extended it from single-region tables to multi-regional models with spillover analysis, and 1.1.0 immediately corrected the naming and measures that release introduced, renaming shock-origin columns that had been labelled as destinations and replacing an interdependence index with spillover balance and export share.

◆ Where it's heading

The package built its foundation first and its scope second. The 0.1.x releases were almost entirely about making a Rust-backed R package install reliably across platforms and toolchain versions, with the actual economics settled at 0.1.0. Once that was stable, 1.0.0 added the multi-regional layer in one release, and 1.1.0 shows the usual consequence of a large surface arriving at once: names and derived measures needing correction before they harden. Breaking changes are being taken freely while the multi-regional interface is young.

◆ Prediction

Expect further refinement of the multi-regional measures before the interface settles, given that 1.1.0 revised them within three months of their introduction. The Rust core makes larger multi-regional systems tractable, so extending coverage to more published multi-region tables is the obvious direction, though these entries name no specific dataset.

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 fio 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 fio or mice.

See all fio alternatives → · See all mice alternatives →

Recent activity from fio and mice

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

  1. 2mo agofioSpillover columns renamed to name the shock's origin
  2. 5mo agofioMulti-regional input-output models and spillover analysis
  3. 8mo agomicemice 3.19.0
  4. 1y agomicemice 3.18.0
  5. 1y agofioToolchain fixes for R-devel and Windows linking
  6. 1y agomicemice 3.17.0
  7. 1y agofioSystem check scripts handle a missing Rust toolchain
  8. 1y agofioRust minimum version lowered to widen installability
  9. 2y agofioFirst release: Rust-backed input-output modelling in R
  10. 3y agomicemice 3.16.0
  11. 3y agomicemice 3.15.0
  12. 4y agomicemice 3.14.0

Frequently asked questions

What is the difference between fio and mice?

Both compete on the same themes — r-package — within Infra & APIs. fio and mice 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 fio better than mice?

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

What are the best alternatives to fio?

Top fio alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "fio alternatives" section above for the current picks, or visit /alternatives/fio 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.