HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
A side-by-side editorial comparison of BayLum and sps — release velocity, themes, recent moves, and the top alternatives to consider.
Bayesian luminescence dating that finally replaced its folder-structure input format.
BayLum runs Bayesian age models for luminescence and combined OSL/C-14 dating on top of JAGS. The 2024 release rebuilt the front end: a single create_DataFile() replaces the separate single-grain and multi-grain generators, reads BIN/BINX and XSYG directly, and takes a YAML config in place of the old prescribed folder layout. Since then the work has been CRAN compliance and documentation.
sps keeps sanding down sequential Poisson sampling rather than adding to it.
The package implements sequential Poisson sampling for survey design, covering inclusion probabilities, proportional allocation, replicate weights, and take-all strata. Recent releases are small and tightly scoped: a divisor method helper, an iterator that draws a sample one unit at a time, automatic selection of the replicate-weight parameter, and repeated performance work on inclusion probability calculation.
BayLum runs Bayesian age models for luminescence and combined OSL/C-14 dating on top of JAGS. The 2024 release rebuilt the front end: a single create_DataFile() replaces the separate single-grain and multi-grain generators, reads BIN/BINX and XSYG directly, and takes a YAML config in place of the old prescribed folder layout. Since then the work has been CRAN compliance and documentation.
Two long-running threads have converged: making JAGS runs survivable (parallel methods, halved MCMC memory, injectable custom models) and making the inputs survivable (YAML config, consistency checks, auto-detected sample names). With the deprecated generators on their way out, the next phase is removal rather than addition. Release cadence is roughly annual and slowing.
The deprecated Generate_DataFile(), Generate_DataFile_MG() and LT_RegenDose() are the obvious next casualties; a release that drops them would be the first breaking change since the YAML rework.
The package implements sequential Poisson sampling for survey design, covering inclusion probabilities, proportional allocation, replicate weights, and take-all strata. Recent releases are small and tightly scoped: a divisor method helper, an iterator that draws a sample one unit at a time, automatic selection of the replicate-weight parameter, and repeated performance work on inclusion probability calculation.
Development is consolidation rather than expansion. Most releases either speed up an existing routine or remove a decision the user previously had to make by hand, such as picking the smallest parameter that keeps replicate weights non-negative. Documentation and tooling get comparable attention to the algorithms, with a dedicated vignette on inclusion probabilities and a recent switch of test and documentation infrastructure. The API surface has been essentially stable across the window.
Expect continued small ergonomic and performance releases against the existing function set rather than new sampling designs, which is the pattern every release in this window follows.
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 BayLum or sps.
France's national flood statistics, ported out of Fortran and into R.
Sign, zero and narrative restrictions brought into the bsvars ecosystem.
Fast design-based estimators for experiments, coasting on CRAN patches.
The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.
IP address vectors for R that hit 1.0 and then went quiet.
A column-key toolkit for stitching decades of ecological field data into one table.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. sps is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. 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. sps is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
Top BayLum alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "BayLum alternatives" section above for the current picks, or visit /alternatives/baylum for the full list with editorial commentary on each.
Top sps alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "sps alternatives" section above for the current picks, or visit /alternatives/sps for the full list with editorial commentary on each.