datefixR
The messy-date parser rewrote its core in Rust and came out 300x faster.
A side-by-side editorial comparison of fellingdater and mirai — 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.
mirai removed its dispatcher process and added memory backpressure to the queue.
The async evaluation framework releases roughly monthly and moves fast at the architecture level. In 2.7.0 the dispatcher stopped being a separate process and became a thread, after its loop had already been rewritten in C inside nanonext one release earlier. The same release added an opt-in memory budget for queued task payloads and try_mirai(), which returns NULL immediately rather than blocking when that budget is exhausted.
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.
The async evaluation framework releases roughly monthly and moves fast at the architecture level. In 2.7.0 the dispatcher stopped being a separate process and became a thread, after its loop had already been rewritten in C inside nanonext one release earlier. The same release added an opt-in memory budget for queued task payloads and try_mirai(), which returns NULL immediately rather than blocking when that budget is exhausted.
Two threads of work run together: cutting overhead out of the task path — thread-based dispatcher, in-process transport for synchronous daemons, lower per-element dispatch cost in mirai_map() — and making the framework safe to embed in an event loop, where blocking the host R thread is not acceptable. Deployment reach is growing too, with http_config() launching remote daemons over HTTP APIs and auto-configuring for Posit Workbench. Each release pins a minimum nanonext version, so the two packages advance as one unit.
With backpressure in place but opt-in, the open question these notes leave is whether a default memory budget arrives; continued overhead reduction and Shiny-facing non-blocking paths are the safer bet.
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 mirai.
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 mirai alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. mirai 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. mirai 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 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 mirai alternatives in Analytics are ranked by recent ship velocity. Browse the "mirai alternatives" section above for the current picks, or visit /alternatives/mirai for the full list with editorial commentary on each.