humind
The MSNI humanitarian needs framework as code, rewritten and re-broken every year
A side-by-side editorial comparison of echos and inlabru — release velocity, themes, recent moves, and the top alternatives to consider.
Echo state networks for R forecasting, filling in the pieces a fable model is expected to have.
echos fits echo state networks, a reservoir-computing approach to time series forecasting, and exposes them through the fabletools model interface so they sit alongside other models in a fable workflow. The three releases in this window take it from a working model to a complete one: forecast intervals in 1.0.2, hyperparameter tuning by rolling-origin cross-validation in 1.0.3, and documentation covering the architecture, hyperparameters and tuning workflow in 1.0.4. Cadence is a few releases a year.
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.
echos fits echo state networks, a reservoir-computing approach to time series forecasting, and exposes them through the fabletools model interface so they sit alongside other models in a fable workflow. The three releases in this window take it from a working model to a complete one: forecast intervals in 1.0.2, hyperparameter tuning by rolling-origin cross-validation in 1.0.3, and documentation covering the architecture, hyperparameters and tuning workflow in 1.0.4. Cadence is a few releases a year.
The arc here is a model implementation earning its place in an established framework. Point forecasts came first, then the interval forecasts that any fable-compatible model is expected to produce, generated by bootstrapping residuals and taking quantiles from simulated paths, then the tuning machinery that makes the reservoir hyperparameters usable by people who do not already know what alpha and rho do. Version 1.0.4 spending its whole release on documentation and a clearer dataset name is consistent with that: the remaining barrier is comprehension, not capability.
With intervals and tuning in place, the natural next step is broader integration with the fable ecosystem, such as handling multiple series or ensembling with other model types. The entries do not indicate whether the maintainer intends to go further into reservoir variants or to stabilise what is here.
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 echos 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.
See all echos alternatives → · See all inlabru alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. 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 echos alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "echos alternatives" section above for the current picks, or visit /alternatives/echos 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.