Basedash
Basedash keeps pushing its data out of the workspace — now to people without accounts
A side-by-side editorial comparison of distributions3 and segregatr — release velocity, themes, recent moves, and the top alternatives to consider.
| Feature | distributions3 | segregatr |
|---|---|---|
| Sector | Analytics | Analytics |
| Velocity score | 6.3 | 0.0 |
| Sparks · 30d | 1 | 0 |
| Top themes | r-package, probability-distributions, empirical-distributions, likelihood-inference | statistical-genetics, pedigree-analysis, variant-classification, pedsuite |
| Last editorial update | 1h ago | 3d ago |
| Website | Visit → | Visit → |
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.
A segregation-analysis tool that keeps widening which pedigrees it can actually handle.
segregatr computes full-likelihood Bayes factors for variant segregation in families, built on pedtools and part of the wider pedsuite ecosystem. Its releases are infrequent but each one lifts a structural restriction: loops, recessive and X-linked models, liability classes, and most recently a proband-free variant of the score. The companion shinyseg app gives the same machinery a clinical front end.
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.
segregatr computes full-likelihood Bayes factors for variant segregation in families, built on pedtools and part of the wider pedsuite ecosystem. Its releases are infrequent but each one lifts a structural restriction: loops, recessive and X-linked models, liability classes, and most recently a proband-free variant of the score. The companion shinyseg app gives the same machinery a clinical front end.
The through-line is coverage of awkward real-world pedigrees rather than new statistics. Loops were handled for the core score in 0.3.0 and then extended to liability classes in 0.4.0, so the same structural capability is being pushed through the codebase feature by feature. The 2025 release moves in a different direction, relaxing the requirement for a designated proband. Development is slow and steady, roughly annual, and tracks its pedtools dependency closely.
Expect the next release to continue relaxing modelling constraints — the pattern of retrofitting each new capability across loops, liability classes and inheritance models is unfinished — rather than expanding beyond segregation scoring.
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 segregatr.
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See all distributions3 alternatives → · See all segregatr 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 segregatr alternatives in Analytics are ranked by recent ship velocity. Browse the "segregatr alternatives" section above for the current picks, or visit /alternatives/segregatr for the full list with editorial commentary on each.