rollupTree
The recursive-computation engine under massProps grows the accessors its consumer needed
A side-by-side editorial comparison of serocalculator and smam — release velocity, themes, recent moves, and the top alternatives to consider.
A seroincidence engine grows up: whole API renamed, then clustered survey designs
serocalculator turns cross-sectional antibody measurements into infection-rate estimates, and it spent its last two releases making itself safe to depend on. Version 1.4.0 renamed nearly every user-facing function into a consistent est_seroincidence()/sr_params vocabulary; 1.4.1 shipped the migration crosswalk that admits how much that broke. The newest capability is cluster-robust variance estimation for household- and school-based surveys.
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.
serocalculator turns cross-sectional antibody measurements into infection-rate estimates, and it spent its last two releases making itself safe to depend on. Version 1.4.0 renamed nearly every user-facing function into a consistent est_seroincidence()/sr_params vocabulary; 1.4.1 shipped the migration crosswalk that admits how much that broke. The newest capability is cluster-robust variance estimation for household- and school-based surveys.
The arc runs from method to instrument. Early releases added example data and plotting; recent ones fix the API surface, satisfy CRAN's offline-failure policy, and extend the estimator to sampling designs field epidemiology actually uses — multi-level clustering, stratification, and the two combined. Each release also carries visible refactoring discipline (one function per file, linting, per-PR website previews) that reads like a package preparing for contributors it does not have yet.
With cluster_var and stratum_var now threaded through both est_seroincidence() and est_seroincidence_by(), survey weights are the remaining piece of a complex-survey design the sandwich estimator does not cover. The entries do not name it, so read that as direction rather than a promise.
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 serocalculator or smam.
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
A black-box interpreter reaches CRAN, then learns multi-class and survival responses
See all serocalculator alternatives → · See all smam alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. serocalculator 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. serocalculator 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 serocalculator alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "serocalculator alternatives" section above for the current picks, or visit /alternatives/serocalculator 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.