vcfR
A genomics workhorse whose visible release feed stops dead in mid-2020.
A side-by-side editorial comparison of DMRnet and tf — release velocity, themes, recent moves, and the top alternatives to consider.
A categorical-variable selection package that publishes its full test logs as release candidates.
DMRnet implements delete-or-merge-regressors model selection for high-dimensional categorical data, alongside SOSnet and GLAMER variants from the same research group. Development is slow and academic — 0.4.0 in 2023, then two years to 0.4.1 in August 2025, which corrects an invalid lambda.1se computation and the cross-validation plots that displayed it. Every real release is preceded days earlier by a release-candidate entry containing the raw output of the correctness and consistency test suite.
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.
DMRnet implements delete-or-merge-regressors model selection for high-dimensional categorical data, alongside SOSnet and GLAMER variants from the same research group. Development is slow and academic — 0.4.0 in 2023, then two years to 0.4.1 in August 2025, which corrects an invalid lambda.1se computation and the cross-validation plots that displayed it. Every real release is preceded days earlier by a release-candidate entry containing the raw output of the correctness and consistency test suite.
The package is converging on correctness rather than expanding. 0.3.3 was a wall of fixes to inference, log-likelihood, and degenerate cross-validation cases; 0.4.0 added the var_sel algorithm and brought GLAMER into the package's own net idiom over its tau parameter; 0.4.1 is again a statistical correctness fix. The published test-log releases are the tell — this maintainer treats reproducible evidence that hard cases still pass as part of the release artifact, which is unusual outside academic statistical software.
Given the two-year gap before 0.4.1 and its narrow scope, the next release is most likely another correctness fix arriving on a multi-year cadence, again preceded by a full test-log release candidate.
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 DMRnet or tf.
A genomics workhorse whose visible release feed stops dead in mid-2020.
The R phone-number package stopped only parsing numbers and started asking them where they are.
A Venn diagram package whose public release notes say almost nothing — including about its copyright cleanup.
A Shiny app for choosing the right ordinal test reached CRAN in a single 90-minute burst of tags.
A new R localization package that reached CRAN and immediately downgraded itself to experimental.
An R client for HERE's location APIs, shaped almost entirely by what the vendor exposes next.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. DMRnet 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. DMRnet 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 DMRnet alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "DMRnet alternatives" section above for the current picks, or visit /alternatives/dmrnet 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.