FoRecoML
The machine-learning arm of a forecast reconciliation toolkit, four months old and already sharing its sibling's plumbing.
A side-by-side editorial comparison of ggdist and projoint — release velocity, themes, recent moves, and the top alternatives to consider.
The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.
ggdist supplies ggplot2 with a compositional vocabulary for distributions — slabs, intervals, dotplots and the sub-geometries that combine them. The last three years moved it from a drawing library to an estimation library: bounded density estimation with Sheather-Jones bandwidth became the default, weights propagate through every density, interval and point summary, and blurred dotplots render Monte Carlo standard error as visual fuzz. The 2025 release rounds this out with per-geometry thickness subscales and settable global defaults.
projoint spent a year on CRAN paperwork, then shipped a correctness fix it flagged itself.
projoint is an R package for analysing conjoint survey experiments, covering Qualtrics import, reshaping, and quantity-of-interest estimation with inter-rater reliability correction. Most of its release history is CRAN admission work — citation formats, DESCRIPTION fields, \value{} tags, vignette cleanups — with four tags backfilled within ninety seconds of each other on 15 July in non-monotonic version order, so neither tag order nor timestamps in this feed track the real sequence. The substantive releases are the ones fixing data-preparation bugs that silently corrupt estimates.
ggdist supplies ggplot2 with a compositional vocabulary for distributions — slabs, intervals, dotplots and the sub-geometries that combine them. The last three years moved it from a drawing library to an estimation library: bounded density estimation with Sheather-Jones bandwidth became the default, weights propagate through every density, interval and point summary, and blurred dotplots render Monte Carlo standard error as visual fuzz. The 2025 release rounds this out with per-geometry thickness subscales and settable global defaults.
Two threads run in parallel and keep converging. One is statistical: pluggable density estimators arrived first, then became the default, then gained weights and quantile histograms. The other is compositional: sub-geometries acquired their own guides, then their own scales, so a slab's thickness axis is now a first-class annotated dimension. Cadence has stretched from twice-yearly to roughly annual, with the recent work tightening existing surface rather than opening new.
Subguides gained subscales a release later, so the remaining asymmetry is in the sub-geometry system rather than the statistics; expect the next release to continue that fill-in work.
projoint is an R package for analysing conjoint survey experiments, covering Qualtrics import, reshaping, and quantity-of-interest estimation with inter-rater reliability correction. Most of its release history is CRAN admission work — citation formats, DESCRIPTION fields, \value{} tags, vignette cleanups — with four tags backfilled within ninety seconds of each other on 15 July in non-monotonic version order, so neither tag order nor timestamps in this feed track the real sequence. The substantive releases are the ones fixing data-preparation bugs that silently corrupt estimates.
The maintainer is hardening the path from raw Qualtrics export to estimate, which is where conjoint analysis quietly goes wrong. Three separate releases fix that path: dropped respondent-level weights in organize_data(), repeated-task reshaping in reshape_projoint(), and choice-to-profile mapping in 1.1.3. Each fix now arrives with regression tests and stricter validation rather than just a patch, and 1.1.3 adds an explicit .choice_map so the mapping is auditable instead of inferred.
Expect the validation-and-regression-test pattern to keep extending across the import path, with releases continuing to arrive in bursts around CRAN submission rather than on a cadence.
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 ggdist or projoint.
The machine-learning arm of a forecast reconciliation toolkit, four months old and already sharing its sibling's plumbing.
Forecast reconciliation with a real object model, five years after it started returning bare matrices.
A textbook data package whose whole job is to stay installable, and whose releases prove how much work that is.
A graph-centrality package that spent 2026 making its existing measures usable at scale, then went quiet.
A test-theory package that grew into a graphical-model toolkit, now spending its releases paying down the API debt that growth created.
nuggets keeps compounding on the 2.0 rewrite — more pattern families, lighter install.
See all ggdist alternatives → · See all projoint alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. projoint 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. projoint 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 Infra & APIs products to evaluate alongside.
Top ggdist alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ggdist alternatives" section above for the current picks, or visit /alternatives/ggdist for the full list with editorial commentary on each.
Top projoint alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "projoint alternatives" section above for the current picks, or visit /alternatives/projoint for the full list with editorial commentary on each.