STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of fastrg and rnpn — release velocity, themes, recent moves, and the top alternatives to consider.
A fast random-graph sampler that spent 0.3.1 fixing what its parameters actually mean.
fastRG samples from generalized random dot product graphs — stochastic blockmodels, degree-corrected and overlapping variants, directed and undirected — in time proportional to the number of edges rather than nodes squared, which is what makes large sparse networks tractable. Since 0.3.1 the model is constructed and parameterised in one object, with sampling methods taking that object rather than re-specifying edge distribution at sample time.
The USA phenology data client rebuilt its entire stack and stopped handing users -9999 as a number.
rnpn is the R client for the USA National Phenology Network, retrieving observation records, phenometrics and gridded model layers. Version 1.3.0 in March 2025 replaced nearly all of its infrastructure at once — sp and raster dropped, terra made optional, XML swapped for xml2, plyr for dplyr, httr and curl for httr2 — and changed what functions return, with tibbles in place of data.tables and empty tibbles in place of NULL on error. The two releases since have completed the missing-value handling and restored performance lost in the transition.
fastRG samples from generalized random dot product graphs — stochastic blockmodels, degree-corrected and overlapping variants, directed and undirected — in time proportional to the number of edges rather than nodes squared, which is what makes large sparse networks tractable. Since 0.3.1 the model is constructed and parameterised in one object, with sampling methods taking that object rather than re-specifying edge distribution at sample time.
The package's development has been about semantic correctness more than speed. The 0.3.1 release moved edge-distribution arguments to the constructors and reinterpreted the mixing matrix S under Bernoulli parameterisation; 0.3.2 then flipped the meaning of X and Y in directed blockmodels so outgoing and incoming factors match the edge convention, and made block sorting conditional rather than unconditional. Both are corrections to what returned values mean, not to how fast they arrive. The 2025 release is CRAN documentation linking only.
With parameterisation settled and only a documentation release since 2023, the package reads as feature-complete for its sampling families. Nothing in the entries points to additional model types being queued.
rnpn is the R client for the USA National Phenology Network, retrieving observation records, phenometrics and gridded model layers. Version 1.3.0 in March 2025 replaced nearly all of its infrastructure at once — sp and raster dropped, terra made optional, XML swapped for xml2, plyr for dplyr, httr and curl for httr2 — and changed what functions return, with tibbles in place of data.tables and empty tibbles in place of NULL on error. The two releases since have completed the missing-value handling and restored performance lost in the transition.
The package is being brought onto the current R stack and made honest about missing data, and those are the same project. Converting the -9999 sentinel to NA started in 1.3.0 for download functions and was extended to all columns in 1.4.1; the string "emptyvalue" got the same treatment. Beyond the migration, the feature additions are modest and specific to the domain, such as custom start and end dates for defining a phenometrics season.
With the dependency migration finished and sentinel handling now applied across all columns, the next releases most likely return to domain features and to fixes surfaced by the server side, which has already prompted work through migrations and backend moves. The removed progress indicator is an acknowledged regression that may come back.
Other Analytics 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 fastrg or rnpn.
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. fastrg and rnpn 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. fastrg and rnpn 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 Analytics products to evaluate alongside.
Top fastrg alternatives in Analytics are ranked by recent ship velocity. Browse the "fastrg alternatives" section above for the current picks, or visit /alternatives/fastrg for the full list with editorial commentary on each.
Top rnpn alternatives in Analytics are ranked by recent ship velocity. Browse the "rnpn alternatives" section above for the current picks, or visit /alternatives/rnpn for the full list with editorial commentary on each.