humind
The MSNI humanitarian needs framework as code, rewritten and re-broken every year
A side-by-side editorial comparison of coga and echos — release velocity, themes, recent moves, and the top alternatives to consider.
A gamma-convolution density package that reached completion in 2018 and has coasted since.
coga computes densities, distribution functions and random numbers for convolutions of gamma distributions, with the numerical work in C++ through Rcpp. It has been feature-complete since 1.0.0 in 2018, and every release in the seven years since has been maintenance: a documentation alias for CRAN, a compiler warning, a maintainer email change, and an Rcpp update requiring Rf_error calls to be guarded. The one functional addition in that period, in 1.1.0, was an unexported function added explicitly for research use.
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.
coga computes densities, distribution functions and random numbers for convolutions of gamma distributions, with the numerical work in C++ through Rcpp. It has been feature-complete since 1.0.0 in 2018, and every release in the seven years since has been maintenance: a documentation alias for CRAN, a compiler warning, a maintainer email change, and an Rcpp update requiring Rf_error calls to be guarded. The one functional addition in that period, in 1.1.0, was an unexported function added explicitly for research use.
This is a finished package being kept alive rather than developed. The releases track external pressure exactly: CRAN documentation requirements, compiler warnings, Rcpp API changes. Its maintenance is visibly shared with smam, the same maintainer's animal-movement package, which received the same email update, the same format-security fix and the same Rcpp guard within a minute or twenty of coga each time. Neither package is being extended; both are being kept installable.
Expect nothing but CRAN and toolchain maintenance, arriving whenever Rcpp or R's check requirements change, and arriving alongside smam. There is no signal in these entries of planned functional work.
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.
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 coga or echos.
The MSNI humanitarian needs framework as code, rewritten and re-broken every year
A Bayesian spatial modelling package rebuilding its foundations one breaking release at a time
UK government chart styling in ggplot2, chasing ggplot2 v4 and stretching its palette to five.
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.
They serve adjacent needs but don't currently overlap on shipped themes. coga and echos 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. coga and echos 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 coga alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "coga alternatives" section above for the current picks, or visit /alternatives/coga for the full list with editorial commentary on each.
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.