epinowcast
epinowcast added Gaussian processes to its formula interface and made the sampler twice as fast
A side-by-side editorial comparison of glyread and ribd — release velocity, themes, recent moves, and the top alternatives to consider.
glyread now hands every importer's output straight to Bioconductor.
glyread is the import layer, converting output from pGlyco3, Byonic, GlycanFinder, GlyHunter, and pGlycoQuant into glycoverse objects. Version 0.12.0 changed what those objects are: every read_*() function now returns GlycomicSE or GlycoproteomicSE, and 0.12.1 raised the glyexp floor to 0.16.0 to match. Earlier releases in the window went to format handling, particularly multi-glycosite glycopeptides and linkage-specific derivatization presets.
The pedsuite's coefficient engine: broadening what it computes, then making the plots publishable.
ribd computes relatedness coefficients from pedigrees, covering kinship, inbreeding, kappa, condensed and detailed identity coefficients, and two-locus versions of several of these, in autosomal and X-chromosomal form. The IBD triangle is now drawable in base graphics, ggplot2 or plotly, with an optional inset pedigree, and custom relationships can be placed on it. The most recent release is dominated by correctness work, fixing pair ordering and row alignment in coefficient tables and edge cases for pedigree lists, unrelated individuals and self-pairs.
glyread is the import layer, converting output from pGlyco3, Byonic, GlycanFinder, GlyHunter, and pGlycoQuant into glycoverse objects. Version 0.12.0 changed what those objects are: every read_*() function now returns GlycomicSE or GlycoproteomicSE, and 0.12.1 raised the glyexp floor to 0.16.0 to match. Earlier releases in the window went to format handling, particularly multi-glycosite glycopeptides and linkage-specific derivatization presets.
As the stack's entry point, glyread absorbs container decisions first and hardest: because it constructs the objects everything downstream consumes, it had no compatibility path and simply switched return types. The other visible thread is coverage of upstream software, adding importers and presets as new search engines and protocols appear. Those two threads rarely interact.
Expect the next releases to return to importer coverage, adding formats or presets, now that the container question is settled at the source.
ribd computes relatedness coefficients from pedigrees, covering kinship, inbreeding, kappa, condensed and detailed identity coefficients, and two-locus versions of several of these, in autosomal and X-chromosomal form. The IBD triangle is now drawable in base graphics, ggplot2 or plotly, with an optional inset pedigree, and custom relationships can be placed on it. The most recent release is dominated by correctness work, fixing pair ordering and row alignment in coefficient tables and edge cases for pedigree lists, unrelated individuals and self-pairs.
The arc runs from generality to presentation to precision. Early releases replaced narrow functions with general ones, most visibly when gKinship() absorbed generalisedKinship() and identityCoefs() superseded the separate autosomal and X-chromosomal identity functions in favour of an Xchrom argument. The middle stretch turned the IBD triangle into a proper plotting surface across three graphics systems. The current phase reads as consolidation, with the newest release listing six bug fixes against four features, several of them alignment errors in output tables, which is where a coefficient library most needs to be exactly right.
The two new internal functions in the latest release, inbreedingContributions() and ancestralKinship(), are the kind of thing that surfaces publicly a release or two later, so expect them to become exported decomposition tools. The correctness push through pedigree lists and edge cases suggests the near-term focus stays on hardening rather than new coefficient families.
Other Analytics 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 glyread or ribd.
epinowcast added Gaussian processes to its formula interface and made the sampler twice as fast
duckspatial rebuilt itself around a lazy DuckDB class, then spent four releases filling in the geometry surface
Three years dormant, timbr returns with dplyr verbs that finally respect tree structure
gps2gtfs spent a release making its docs stop describing functions it does not have.
ducksemantics puts an ontology graph and ColBERT retrieval inside DuckDB, callable from R.
dvir keeps making disaster victim identification a single call instead of a workflow.
See all glyread alternatives → · See all ribd alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ribd is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. 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. ribd is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top glyread alternatives in Analytics are ranked by recent ship velocity. Browse the "glyread alternatives" section above for the current picks, or visit /alternatives/glyread for the full list with editorial commentary on each.
Top ribd alternatives in Analytics are ranked by recent ship velocity. Browse the "ribd alternatives" section above for the current picks, or visit /alternatives/ribd for the full list with editorial commentary on each.