rollupTree
The recursive-computation engine under massProps grows the accessors its consumer needed
A side-by-side editorial comparison of prospectr and writeAlizer — release velocity, themes, recent moves, and the top alternatives to consider.
prospectr spent its biggest release in years fixing spectra it had been quietly mangling.
prospectr provides the signal-processing layer for near-infrared and visible spectroscopy in R — Savitzky-Golay and gap-segment derivatives, standard normal variate, detrending, continuum removal, splice correction, plus calibration sampling algorithms like Kennard-Stone and DUPLEX and readers for ASD and BUCHI NIRCal instrument files. The May release is the substantial one: a long list of corrections to functions that were returning wrong or missing values rather than failing.
Six months of releases and not one of them touched the scoring models
writeAlizer generates predicted writing-quality scores from features produced by Coh-Metrix, ReaderBench and GAMET, downloading its trained scoring models on demand. Every release in this window — nine of them between September 2025 and February 2026 — is about that download path rather than the scoring: classed error conditions, checksum verification, an offline mode, a mockable artifact directory, and dependency reporting for the model families a user actually invokes.
prospectr provides the signal-processing layer for near-infrared and visible spectroscopy in R — Savitzky-Golay and gap-segment derivatives, standard normal variate, detrending, continuum removal, splice correction, plus calibration sampling algorithms like Kennard-Stone and DUPLEX and readers for ASD and BUCHI NIRCal instrument files. The May release is the substantial one: a long list of corrections to functions that were returning wrong or missing values rather than failing.
The recent work is corrective rather than additive, and several items changed results silently before being caught. continuumRemoval() derived its convex-hull boundary offset from a fixed one-wavelength assumption that broke for fine-resolution spectra or non-nanometre units; cochranTest() passed an invalid argument name to prcomp() and produced incorrect principal component scores; readASD() silently dropped spectra in one branch of its text path. Two file readers were leaking connections. Alongside that runs a smaller thread of decoupling preprocessing steps from each other, most visibly detrend() gaining an snv argument so polynomial detrending can run without the SNV transform that Barnes et al. bundled with it.
The detrend() decoupling is the only recent addition and it fits a broader pipeline-composition direction, so similar separation of other bundled preprocessing steps is the plausible next move. The misspelled substraction argument now carries a deprecation warning, which schedules its removal for a future release.
writeAlizer generates predicted writing-quality scores from features produced by Coh-Metrix, ReaderBench and GAMET, downloading its trained scoring models on demand. Every release in this window — nine of them between September 2025 and February 2026 — is about that download path rather than the scoring: classed error conditions, checksum verification, an offline mode, a mockable artifact directory, and dependency reporting for the model families a user actually invokes.
The package is being made safe to distribute. CRAN's policy on packages that reach the internet drove the first wave — graceful failure, tests that preflight their URLs and skip, examples seeded from a local mock model — and 1.7.0 turned the accumulated fixes into structure with named error classes for each failure mode. Only 1.7.2 adds anything a user would ask for: filename handling for Coh-Metrix and GAMET outputs that arrive as paths.
With the artifact registry hardened and documented, the pressure that produced nine releases in six months should ease, and attention can return to the models themselves — the vignette on scoring-model development added in 1.7.2 hints at that. Nothing here promises new models.
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 prospectr or writeAlizer.
The recursive-computation engine under massProps grows the accessors its consumer needed
A mass-properties rollup spends a year on documentation and follows its sibling's API
A cognitive-science sampling package ships once, then goes quiet for eighteen months
A Bayesian volatility sampler in its maintenance decade, paying for its own speed
A black-box interpreter reaches CRAN, then learns multi-class and survival responses
Spatial thinning grows a result object, and the API breaks to make room for it
See all prospectr alternatives → · See all writeAlizer alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. prospectr and writeAlizer 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. prospectr and writeAlizer 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 Infra & APIs products to evaluate alongside.
Top prospectr alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "prospectr alternatives" section above for the current picks, or visit /alternatives/prospectr for the full list with editorial commentary on each.
Top writeAlizer alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "writeAlizer alternatives" section above for the current picks, or visit /alternatives/writealizer for the full list with editorial commentary on each.