Whatagraph
Whatagraph keeps fixing what breaks when one account runs a thousand sources.
A side-by-side editorial comparison of distributions3 and sass — release velocity, themes, recent moves, and the top alternatives to consider.
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.
Nine releases of compiler warnings and CRAN checks — the Sass binding is in pure upkeep.
sass compiles Sass to CSS for R, and its recent history is almost entirely about staying installable. The last ten releases are dominated by compilation warnings on new toolchains — Apple Clang 15, gcc-12, Windows — plus R CMD check fixes for r-devel and one LibSass version bump. The only user-visible changes in the set are the switch to woff2 font files in font_google(local = TRUE) and clearer output when Google fonts are downloaded.
An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.
Growth used to arrive as new distribution families contributed from outside - the extreme-value set, Erlang, later the Poisson binomial. This release changes the axis: alongside two new distributions it adds an inference layer (score, hessian) and a forecast-evaluation one (crps() methods against scoringRules), which are capabilities about distributions rather than more of them. Dependency weight is being cut at the same time, with ggplot2 demoted to Suggests and glue replaced by base R sprintf().
With numeric fallbacks and the derivative generics in place, expect analytic score() and hessian() methods to be filled in across more of the distribution catalogue. The constructor-default change is the likeliest source of follow-up fixes, since calls like Poisson() now return a length-zero distribution where they previously errored.
sass compiles Sass to CSS for R, and its recent history is almost entirely about staying installable. The last ten releases are dominated by compilation warnings on new toolchains — Apple Clang 15, gcc-12, Windows — plus R CMD check fixes for r-devel and one LibSass version bump. The only user-visible changes in the set are the switch to woff2 font files in font_google(local = TRUE) and clearer output when Google fonts are downloaded.
This is a stable binding to a C++ library that is itself no longer moving, so the package's work is defined by the compilers and CRAN policies around it rather than by Sass features. Nothing in these entries suggests active development; the maintenance is competent and prompt, but it is maintenance.
The next release will most likely be triggered by a new compiler warning class or an R CMD check requirement rather than by anything in the Sass language. The entries give no signal of planned feature work.
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 distributions3 or sass.
Whatagraph keeps fixing what breaks when one account runs a thousand sources.
Lightdash keeps handing authoring to outside agents and keeping the governed layer for itself.
A 4.4.0 tag appears, but the feed carries only its release plumbing
Basedash keeps pushing its data out of the workspace — now to people without accounts
RStudio ships through release branches, and the notes are commit messages
dbt Fusion's second beta is adapter work: ClickHouse gets materializations, indexes, and catalogs
See all distributions3 alternatives → · See all sass alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. distributions3 is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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. distributions3 is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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 distributions3 alternatives in Analytics are ranked by recent ship velocity. Browse the "distributions3 alternatives" section above for the current picks, or visit /alternatives/distributions3-r for the full list with editorial commentary on each.
Top sass alternatives in Analytics are ranked by recent ship velocity. Browse the "sass alternatives" section above for the current picks, or visit /alternatives/sass for the full list with editorial commentary on each.