osmapiR
osmapiR is the rare API client that tracks its server's wiki revision numbers in the changelog.
Comparison · Analytics
fellingdater vs nanoparquet
A side-by-side editorial comparison of fellingdater and nanoparquet — release velocity, themes, recent moves, and the top alternatives to consider.
fellingdater vs nanoparquet: at a glance
What is fellingdater?
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.
What is nanoparquet?
nanoparquet is chasing byte-level agreement with the Java and Rust Parquet readers, not feature count.
nanoparquet reads and writes Parquet from R with no Arrow dependency, which is its entire reason to exist. The 0.4.0 line renamed the reader API and added schema authoring plus `append_parquet()`, and the 0.5.x releases have gone after interoperability: definition and repetition level encodings the Apache Parquet Java library expects, flatbuffer alignment the Rust arrow-rs reader expects, 128-bit decimals, and Polars-written files that omit the dictionary page offset. The newest release adds `bit64::integer64` columns and writing to stdout.
fellingdater vs nanoparquet: editorial side-by-side
fellingdater
0.0ANALYTICS
Went from estimating felling dates to doing the crossdating that produces them.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
nanoparquet
0.0ANALYTICS
nanoparquet is chasing byte-level agreement with the Java and Rust Parquet readers, not feature count.
◆ Current state
nanoparquet reads and writes Parquet from R with no Arrow dependency, which is its entire reason to exist. The 0.4.0 line renamed the reader API and added schema authoring plus `append_parquet()`, and the 0.5.x releases have gone after interoperability: definition and repetition level encodings the Apache Parquet Java library expects, flatbuffer alignment the Rust arrow-rs reader expects, 128-bit decimals, and Polars-written files that omit the dictionary page offset. The newest release adds `bit64::integer64` columns and writing to stdout.
◆ Where it's heading
Almost every entry since 0.4.0 names another engine — Java, arrow-rs, Polars, Arrow schema metadata — which tells you the maintainers are treating cross-reader fidelity as the product rather than R-side ergonomics. The type system is filling in from the edges: DECIMAL beyond 8 bytes, UUID, FLOAT16 and INTERVAL as raw lists, and now 64-bit integers with an explicit read-type option instead of a silent cast to double. Writing to `:stdout:` points at a second audience, shell pipelines rather than interactive R.
◆ Prediction
The remaining unmapped Parquet types the changelog has been parking in raw-vector lists — FLOAT16 and INTERVAL — are the obvious next targets, following the same pattern by which DECIMAL and UUID graduated to real R types.
Alternatives to fellingdater and nanoparquet
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 nanoparquet.
ymlthis
ymlthis retired itself, naming Quarto as the reason it no longer needs to exist.
forestly
forestly built an interactive safety review tool, then taught it to produce submission-ready RTF.
pharmaverseadam
pharmaverseadam is the pharmaverse's test-data mirror, and it now covers neurology.
pkglite
pkglite's whole job is knowing which files in an R package are text — and it keeps getting better at guessing.
gMCPLite
gMCPLite exists to be gMCP without Java, and its releases guard that boundary rather than extend it.
See all fellingdater alternatives → · See all nanoparquet alternatives →
Recent activity from fellingdater and nanoparquet
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agonanoparquet64-bit integer columns and writing Parquet to stdout
- 4mo agofellingdaterPolish for the crossdating plots and file reader
- 4mo agonanoparquetFiles now readable by the Java and Rust Parquet libraries
- 1y agofellingdaterAdds a full crossdating and tree-ring analysis toolkit ⚡
- 1y agofellingdaterUser-supplied sapwood data works across all functions
- 1y agofellingdaterFixes fd_report() with user-defined sapwood files
- 1y agonanoparquetReads Polars files that omit the dictionary page offset
- 1y agonanoparquetDate, FLOAT, and mixed-encoding read fixes
- 1y agonanoparquetSchema authoring and append_parquet arrive with a renamed API ⚡
- 1y agonanoparquetFixes a write_parquet crash
- 2y agofellingdaterJOSS paper accepted; citation updated
- 2y agofellingdaterAdds a workflow vignette ahead of JOSS submission
Frequently asked questions
What is the difference between fellingdater and nanoparquet?
They serve adjacent needs but don't currently overlap on shipped themes. fellingdater and nanoparquet 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.
Is fellingdater better than nanoparquet?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fellingdater and nanoparquet 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.
What are the best alternatives to fellingdater?
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.
What are the best alternatives to nanoparquet?
Top nanoparquet alternatives in Analytics are ranked by recent ship velocity. Browse the "nanoparquet alternatives" section above for the current picks, or visit /alternatives/nanoparquet for the full list with editorial commentary on each.