goat
A gene-set enrichment package that outgrew its human-only origins, then went quiet.
A side-by-side editorial comparison of echos and midr — 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 black-box interpreter reaches CRAN, then learns multi-class and survival responses
midr explains black-box models by fitting an interpretable surrogate through Maximum Interpretation Decomposition — main effects plus second-order interactions, with exact Shapley values for the surrogate. Two months after its first CRAN release it can take a matrix response, which covers multi-class classification and survival models, and hold collections of fitted interpretations in midlist and midrib objects for comparison.
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.
midr explains black-box models by fitting an interpretable surrogate through Maximum Interpretation Decomposition — main effects plus second-order interactions, with exact Shapley values for the surrogate. Two months after its first CRAN release it can take a matrix response, which covers multi-class classification and survival models, and hold collections of fitted interpretations in midlist and midrib objects for comparison.
The releases move outward along two axes at once: what can be interpreted, and how much of it fits in memory. Version 0.5.3 rebuilt the fitting path to avoid materialising large design matrices and added a save.memory option; 0.6.0 widened the response from a vector to a matrix and added parametric link functions. Class and argument names were shortened in the same release, so the package is still willing to break itself this early.
With multiple models now held in one object and visualisation methods for them, comparison across models is the surface most likely to fill out next — the collection classes exist but the notes describe manipulation and plotting rather than any comparison metric.
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 midr.
A gene-set enrichment package that outgrew its human-only origins, then went quiet.
The recursive-computation engine under massProps grows the accessors its consumer needed
A mass-properties rollup spends a year on documentation and follows its sibling's API
Six months of releases and not one of them touched the scoring models
A cognitive-science sampling package ships once, then goes quiet for eighteen months
A Bayesian volatility sampler in its maintenance decade, paying for its own speed
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. echos and midr 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. echos and midr 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.
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 midr alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "midr alternatives" section above for the current picks, or visit /alternatives/midr for the full list with editorial commentary on each.