pr2database
The protist reference database keeps widening past the rRNA gene it was built on.
A side-by-side editorial comparison of echos and mice — 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.
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.
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.
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.
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.
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.
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 mice.
The protist reference database keeps widening past the rRNA gene it was built on.
Composable aligned layouts, rebuilt on S7 while ggplot2 4.0 lands underneath.
Conservation planning absorbs the literature's target-setting rules as code.
Joint species distribution models in Gibbs-sampled C++, quiet since 2023.
An ecosystem model starts tracking carbon isotopes and land-use change.
Ten years in, US mapping splits its data out and finally adds Puerto Rico.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. echos 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. echos 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.
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 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.