prioritizr
Conservation planning absorbs the literature's target-setting rules as code.
A side-by-side editorial comparison of PINstimation and tf — 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.
tf gave functional data a second dimension: curves whose values are vectors.
tf supplies the vector classes underneath the tidyfun stack — tfd for raw functional observations, tfb for basis-represented ones, both built on vctrs so curves sit in a data frame column and behave like any other vector. Until July that codomain was scalar. The 0.5.0 release adds tfd_mv and tfb_mv, classes for functions whose values are vectors in R^d, and rebuilds the analysis verbs to match.
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.
tf supplies the vector classes underneath the tidyfun stack — tfd for raw functional observations, tfb for basis-represented ones, both built on vctrs so curves sit in a data frame column and behave like any other vector. Until July that codomain was scalar. The 0.5.0 release adds tfd_mv and tfb_mv, classes for functions whose values are vectors in R^d, and rebuilds the analysis verbs to match.
The package is widening what a functional observation can be, then porting the toolkit onto it. Registration arrived first in 0.4.0 for univariate curves and immediately gained an srvf_mv method for aligning components jointly, and tfb_mfpc() ports principal component analysis to the multivariate case with a single set of scores shared across components. Alongside that runs steady dependency shedding — mvtnorm and pracma both replaced by inlined samplers that reproduce prior draws bit-for-bit, glue dropped for cli in the previous release — and an unusually long tail of NA-handling and edge-case fixes, several caught in pre-release review of the new classes.
The new classes ship with FPCA, registration and shape alignment but the release notes describe tidyfun::tf_unnest() as the consumer of one new export, so the visible next step is the rest of the tidyfun stack catching up to vector-valued columns. Expect follow-up patches on the vctrs casting paths, which is where most of this release's late fixes clustered.
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 tf.
Conservation planning absorbs the literature's target-setting rules as code.
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See all PINstimation alternatives → · See all tf alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. PINstimation and tf 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 tf 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 tf alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "tf alternatives" section above for the current picks, or visit /alternatives/tf for the full list with editorial commentary on each.