humind
The MSNI humanitarian needs framework as code, rewritten and re-broken every year
A side-by-side editorial comparison of predictsr and smam — release velocity, themes, recent moves, and the top alternatives to consider.
A single visible release: the PREDICTS data client patching its download path.
predictsr provides R access to the PREDICTS biodiversity database hosted on the Natural History Museum data portal. Only one release is visible in the feed, 0.2.1, which repairs an API error originating on the portal side and tightens the download path: temporary files are now cleaned up when a download fails, year comparisons no longer compare integers incorrectly, and SHA validation no longer fails on missing values. With one entry there is no cadence to read and no arc to describe.
Animal-movement models in R, where new stochastic processes arrive years apart.
smam fits statistical models of animal movement, covering moving-resting processes with and without measurement error, moving-resting-handling, and moving-moving processes, with simulation, point estimation and variance estimation for each. The last three releases are pure upkeep: guarding Rf_error calls after an Rcpp update, a maintainer email change, and a compiler warning fix. The substantive work in this window is 0.7.0, which added estimate and vcov generics across all fit functions, and 0.6.0, which added the moving-moving process.
predictsr provides R access to the PREDICTS biodiversity database hosted on the Natural History Museum data portal. Only one release is visible in the feed, 0.2.1, which repairs an API error originating on the portal side and tightens the download path: temporary files are now cleaned up when a download fails, year comparisons no longer compare integers incorrectly, and SHA validation no longer fails on missing values. With one entry there is no cadence to read and no arc to describe.
What can be said from a single release is that the package's failure modes are concentrated where it meets the data portal rather than in its own analysis code, and that the fixes are the kind found by users hitting real downloads: leftover temporary files, checksum validation tripping over NAs, an upstream API change. The adoption of the air formatter and a GitHub Actions check in the same release suggests maintenance tooling being put in place rather than a feature programme.
Too little is visible here to predict a direction with any confidence. The one signal worth noting is that this release was largely reactive to a portal-side change, so future releases are likely to track the data portal's behaviour rather than follow a roadmap of the package's own.
smam fits statistical models of animal movement, covering moving-resting processes with and without measurement error, moving-resting-handling, and moving-moving processes, with simulation, point estimation and variance estimation for each. The last three releases are pure upkeep: guarding Rf_error calls after an Rcpp update, a maintainer email change, and a compiler warning fix. The substantive work in this window is 0.7.0, which added estimate and vcov generics across all fit functions, and 0.6.0, which added the moving-moving process.
This package grows by adding process models, and it does so rarely. Between the moving-moving process in 2021 and now, the only interface-level change has been the 0.7.0 generics that gave every fit function a common way to retrieve estimates and their covariance, which is consolidation of an accumulated collection rather than expansion of it. The three releases since are entirely reactive to toolchain and CRAN pressure, and they arrive in step with the maintainer's other package coga, which received the same Rcpp guard within twenty minutes on the same day.
Expect further releases to be CRAN and Rcpp maintenance unless a new movement process is published, which is what has historically prompted a minor version here. The generics added in 0.7.0 give any future process model a ready-made interface to slot into.
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 predictsr or smam.
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.
A gamma-convolution density package that reached completion in 2018 and has coasted since.
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 predictsr alternatives → · See all smam alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — maintenance — within Infra & APIs. predictsr and smam 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. predictsr and smam 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 predictsr alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "predictsr alternatives" section above for the current picks, or visit /alternatives/predictsr for the full list with editorial commentary on each.
Top smam alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "smam alternatives" section above for the current picks, or visit /alternatives/smam for the full list with editorial commentary on each.