mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of PINstimation and samplr — release velocity, themes, recent moves, and the top alternatives to consider.
A market-microstructure toolkit that keeps adding estimators as the papers land.
PINstimation estimates probability-of-informed-trading models — PIN, multilayer PIN, adjusted PIN and VPIN — from trade and quote data, and handles the trade classification and aggregation that feeds them. The current 0.2.0 adds ivpin(), a maximum-likelihood variant of VPIN from Ke and Lin (2017). The package's early history is compressed into a single hour of backfilled tags in October 2022, so version order there does not track release order.
A cognitive-science sampling package ships once, then goes quiet for eighteen months
samplr compares human performance against sampling algorithms, giving cognitive scientists the MCMC machinery to test whether people behave like samplers. Its entire public history is three releases: a 1.0.0 in August 2024, a floating-point fix eighteen seconds later, and then nothing until a February 2026 patch. The release notes are unusually thin even by CRAN standards.
PINstimation estimates probability-of-informed-trading models — PIN, multilayer PIN, adjusted PIN and VPIN — from trade and quote data, and handles the trade classification and aggregation that feeds them. The current 0.2.0 adds ivpin(), a maximum-likelihood variant of VPIN from Ke and Lin (2017). The package's early history is compressed into a single hour of backfilled tags in October 2022, so version order there does not track release order.
Each release tracks the literature: a Bayesian PIN estimator from Griffin et al., an improved VPIN from Ke and Lin, initial-parameter generation realigned to Ersan and Ghachem. The other steady thread is data handling — matrix inputs so the estimators compose with rolling windows, user-specified aggregation frequencies, and now quote leads as well as lags. The three-year gap between 0.1.2 and 0.2.0 makes this a slow, publication-paced package rather than an actively developed one.
On this pattern the next release adds whatever estimator the authors publish next, since two of the three feature releases here implement a specific paper. Nothing in the entries points to a change in the package's structure.
samplr compares human performance against sampling algorithms, giving cognitive scientists the MCMC machinery to test whether people behave like samplers. Its entire public history is three releases: a 1.0.0 in August 2024, a floating-point fix eighteen seconds later, and then nothing until a February 2026 patch. The release notes are unusually thin even by CRAN standards.
The feed shows a package that shipped and stopped. The 2024 tags were both created in one sitting and say almost nothing; the 2026 release is a row-count bug in Mean_Variance() bundled with citation metadata, a dropped dependency and http-to-https link fixes — the housekeeping profile of a package being kept alive for the paper that cites it rather than actively developed.
Adding citation information to the README is usually the move of a maintainer expecting the package to be referenced rather than extended. On this cadence the next release is more likely another CRAN-hygiene patch than new algorithms; there is not enough in these notes to say otherwise.
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 PINstimation or samplr.
mice can finally predict, not just estimate, from multiply imputed data.
A vowel-analysis package trimming dependencies after an email address got it archived.
The R half of the EMU speech database system, fixing what was quietly broken.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
tidyplots keeps rebuilding its own foundations rather than layering around them.
A GENCODE annotation toolkit spent its first year getting out of CRAN's way.
See all PINstimation alternatives → · See all samplr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. PINstimation and samplr 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. PINstimation and samplr 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 PINstimation alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "PINstimation alternatives" section above for the current picks, or visit /alternatives/pinstimation for the full list with editorial commentary on each.
Top samplr alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "samplr alternatives" section above for the current picks, or visit /alternatives/samplr for the full list with editorial commentary on each.