datefixR
The messy-date parser rewrote its core in Rust and came out 300x faster.
A side-by-side editorial comparison of fellingdater and sdtm.oak — release velocity, themes, recent moves, and the top alternatives to consider.
Went from estimating felling dates to doing the crossdating that produces them.
fellingdater estimates when a tree was felled from sapwood measurements, the core inference in dendrochronological dating of timber. Version 1.0.0 passed rOpenSci review with that scope, and the 2024 releases were mostly about the accompanying JOSS paper and user-supplied sapwood datasets. Version 1.2.0 changed the package's remit substantially, adding an entire trs_* family for tree-ring series handling: crossdating with multiple statistical measures, the Hollstein and Baillie-Pilcher t-value transformations, parallel variation percentages, synthetic series generation, and dated-series plotting.
Two releases in, the open-source SDTM toolkit now covers the domains it originally excluded.
sdtm.oak builds SDTM datasets — the tabulation standard clinical trial submissions are filed in — from raw collected data. The 0.1.0 release shipped the mapping algorithm functions and derived-variable helpers but explicitly excluded DM, trial design domains, and several others. Version 0.2.0 closes the largest of those gaps, adding DM domain support via calc_min_max_date() and oak_calc_ref_dates(), plus generate_sdtm_supp() for supplemental qualifier domains.
fellingdater estimates when a tree was felled from sapwood measurements, the core inference in dendrochronological dating of timber. Version 1.0.0 passed rOpenSci review with that scope, and the 2024 releases were mostly about the accompanying JOSS paper and user-supplied sapwood datasets. Version 1.2.0 changed the package's remit substantially, adding an entire trs_* family for tree-ring series handling: crossdating with multiple statistical measures, the Hollstein and Baillie-Pilcher t-value transformations, parallel variation percentages, synthetic series generation, and dated-series plotting.
The package has expanded backwards along the workflow. It began at the last step — given dated series, estimate the felling date — and 1.2.0 added the step before it, establishing those dates by crossdating in the first place. Version 1.2.1 is early polish on that new surface: axis control, non-syntactic column names, encoding safety in read_fh(). The direction is a single package covering the chain from raw ring widths to a felling-date estimate.
Expect the trs_* family to keep accumulating polish and additional crossdating statistics, since it is barely a year old and 1.2.1 was already fixing its plotting and top_n behaviour. Whether the two halves of the package get unified into one workflow interface is the open question the entries do not answer.
sdtm.oak builds SDTM datasets — the tabulation standard clinical trial submissions are filed in — from raw collected data. The 0.1.0 release shipped the mapping algorithm functions and derived-variable helpers but explicitly excluded DM, trial design domains, and several others. Version 0.2.0 closes the largest of those gaps, adding DM domain support via calc_min_max_date() and oak_calc_ref_dates(), plus generate_sdtm_supp() for supplemental qualifier domains.
This is the pharmaverse pattern of building submission tooling in the open, one domain class at a time, with the release history running through GitHub release-candidate tags before each CRAN submission. The direction is clear from the domain checklist: start with the mechanically simple Findings and Events domains, then work toward the ones with cross-dataset dependencies. DM and SUPP were the two that most often forced teams back to bespoke code.
The remaining exclusions from the 0.1.0 scope — trial design domains, SV, SE, RELREC and the EPOCH variable — are the obvious next targets, with EPOCH likely first since it depends on the reference dates 0.2.0 just added. Expect the same rhythm of release candidates ahead of each CRAN submission.
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 fellingdater or sdtm.oak.
The messy-date parser rewrote its core in Rust and came out 300x faster.
The legend engine mapsf spun out, now covering legend types the parent map package can draw.
R help pages translated on demand by whichever LLM you point it at.
Thematic mapping in base R that finally got a theming system, then spent two years polishing legends.
qualtRics moved its contact functions onto XM Directory days before the old endpoints died.
The tidyverts forecasting core rebuilt model combination on full residual covariance.
See all fellingdater alternatives → · See all sdtm.oak alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. fellingdater and sdtm.oak 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. fellingdater and sdtm.oak 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 fellingdater alternatives in Analytics are ranked by recent ship velocity. Browse the "fellingdater alternatives" section above for the current picks, or visit /alternatives/fellingdater for the full list with editorial commentary on each.
Top sdtm.oak alternatives in Analytics are ranked by recent ship velocity. Browse the "sdtm.oak alternatives" section above for the current picks, or visit /alternatives/sdtm-oak for the full list with editorial commentary on each.