humind
The MSNI humanitarian needs framework as code, rewritten and re-broken every year
A side-by-side editorial comparison of fio and inlabru — release velocity, themes, recent moves, and the top alternatives to consider.
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.
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.
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.
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.
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.
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.
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.
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.
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 inlabru.
The MSNI humanitarian needs framework as code, rewritten and re-broken every year
UK government chart styling in ggplot2, chasing ggplot2 v4 and stretching its palette to five.
A gamma-convolution density package that reached completion in 2018 and has coasted since.
Animal-movement models in R, where new stochastic processes arrive years apart.
A basic DNA and RNA sequence toolkit that went quiet for three years, then jumped to 2.0.
Package citation for R documents, quietly growing to meet Quarto.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — breaking-changes, 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.
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.
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.
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.