prioritizr
Conservation planning absorbs the literature's target-setting rules as code.
A side-by-side editorial comparison of BAS and tf — release velocity, themes, recent moves, and the top alternatives to consider.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
BAS performs Bayesian variable selection and model averaging for linear and generalized linear models, sampling from a model space too large to enumerate. The visible history splits cleanly: 2023-2024 added sampling machinery — an adaptive independent MCMC sampler with Horvitz-Thompson estimation, hereditary-constraint counting — while 2.0.0 in late 2025 reworked how sampler output is allocated in C. The 2.0.2 patch is a PROTECT fix for rchk warnings.
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.
BAS performs Bayesian variable selection and model averaging for linear and generalized linear models, sampling from a model space too large to enumerate. The visible history splits cleanly: 2023-2024 added sampling machinery — an adaptive independent MCMC sampler with Horvitz-Thompson estimation, hereditary-constraint counting — while 2.0.0 in late 2025 reworked how sampler output is allocated in C. The 2.0.2 patch is a PROTECT fix for rchk warnings.
Memory is the binding constraint and the releases say so directly. The hereditary-constraint counter, the GROW option, and the replacement of over-allocation with resizing all attack the same problem: n.models is a guess, and guessing high wastes memory on problems where few unique models are actually visited. The 2.0.0 work was additionally forced by R tightening its C API against non-API calls like SETLENGTH, a constraint every C-heavy CRAN package has been absorbing. Method development has been quiet since 1.7.x.
The 1.7.5 notes call the hereditary-constraint counting a first step and say future updates will cover other constraint types, including polynomials, which remain unhandled. That is the one concrete commitment in this history, though nothing since has returned to it.
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 BAS or tf.
Conservation planning absorbs the literature's target-setting rules as code.
Joint species distribution models in Gibbs-sampled C++, quiet since 2023.
An ecosystem model starts tracking carbon isotopes and land-use change.
Ten years in, US mapping splits its data out and finally adds Puerto Rico.
Fitness-tracking analysis in slow maintenance, still absorbing upstream breakage.
State-panel tooling holding steady since its 2020 data and ergonomics release.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. BAS 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. BAS 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 BAS alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "BAS alternatives" section above for the current picks, or visit /alternatives/bas 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.