tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of datasetjson and rstantools — release velocity, themes, recent moves, and the top alternatives to consider.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
The scaffolding layer for Stan-backed R packages, maintained rather than extended.
rstantools generates and maintains the build infrastructure that lets an R package ship Stan models — the inst/stan layout, the auto-generated C++, the Rcpp module loading, and the posterior_* generics downstream packages implement. Its releases are dominated by keeping that scaffolding compiling as Stan, StanHeaders, and rstan move underneath it. Version 2.7.0 continues that pattern, its one user-facing change being an allowance for deprecated syntax in specified versions of specified packages.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
The package's roadmap is not its own — it tracks a CDISC standard that is still moving, and 0.3.0 is what happens when the standard revises: object model, read and write paths, and JSON backend all changed together. Performance was addressed in the same pass, which matters because submission datasets are large enough that a slow serialiser is a real constraint.
The next significant release will most likely follow the next Dataset-JSON schema revision rather than an internal roadmap, given that 0.3.0 was driven entirely by the 1.1.0 update.
rstantools generates and maintains the build infrastructure that lets an R package ship Stan models — the inst/stan layout, the auto-generated C++, the Rcpp module loading, and the posterior_* generics downstream packages implement. Its releases are dominated by keeping that scaffolding compiling as Stan, StanHeaders, and rstan move underneath it. Version 2.7.0 continues that pattern, its one user-facing change being an allowance for deprecated syntax in specified versions of specified packages.
This is a stable dependency in maintenance mode, and the release history reads accordingly: compatibility shims for new rstan and Stan RNG versions, C++ standard bumps, and pkgdown housekeeping. The last substantive API growth was 2.5.0's loo_epred() generic and discrete-data loo_pit(). Contributor churn is visible in recent releases, with several first-time contributors handling infrastructure rather than statistics.
Expect the next releases to continue tracking Stan and rstan breakage as it arrives; nothing in the recent entries points to new generics or a change in the package-generation model.
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 datasetjson or rstantools.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all datasetjson alternatives → · See all rstantools alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. rstantools 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. rstantools 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 Analytics products to evaluate alongside.
Top datasetjson alternatives in Analytics are ranked by recent ship velocity. Browse the "datasetjson alternatives" section above for the current picks, or visit /alternatives/datasetjson for the full list with editorial commentary on each.
Top rstantools alternatives in Analytics are ranked by recent ship velocity. Browse the "rstantools alternatives" section above for the current picks, or visit /alternatives/rstantools for the full list with editorial commentary on each.