JointFPM
Recurrent-event modelling settles, with mean_no() promoted to stable.
A side-by-side editorial comparison of mice and prospectr — release velocity, themes, recent moves, and the top alternatives to consider.
mice can finally predict, not just estimate, from multiply imputed data.
mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.
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.
mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.
Two things are happening. The imputation method catalogue keeps widening — lasso variants, multivariate PMM, categorical PMM via canonical correlation — while the pooling side is being extended past its original purpose, first to synthetic data, now to predictions on held-out sets. That second thread points at predictive modelling workflows rather than the inferential ones mice was built for. Meanwhile the maintainers keep finding consequential old bugs: the augment() ordered-factor defect in 3.18.0 had been silently degrading ordinal imputations for years.
predict_mi() is framed around evaluating predictive performance on test sets, and the ignore argument added in 3.12.0 already exists to hold out rows from the imputation model. Expect the next work to join those up into a fuller train/test story for imputed data, since the pieces are now in place but not yet connected.
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.
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 mice or prospectr.
Recurrent-event modelling settles, with mean_no() promoted to stable.
Nonparametric change point detection swaps p-values for importance scores.
A Prism-styled ggplot2 theme in maintenance, now surviving ggplot2 4.0.
Wavelet trend estimation tightens the defaults it shipped with.
Back from CRAN removal under a new maintainer, with the compiled layer rebuilt.
A market-microstructure toolkit that keeps adding estimators as the papers land.
See all mice alternatives → · See all prospectr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. mice and prospectr 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. mice and prospectr 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 mice alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "mice alternatives" section above for the current picks, or visit /alternatives/mice for the full list with editorial commentary on each.
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.