STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of maths.genealogy and TidyDensity — release velocity, themes, recent moves, and the top alternatives to consider.
| Feature | maths.genealogy | TidyDensity |
|---|---|---|
| Sector | Analytics | Analytics |
| Velocity score | 0.0 | 0.0 |
| Sparks · 30d | 0 | 0 |
| Top themes | academic-genealogy, api-client, graph-visualisation, cran-compliance | statistical-distributions, random-generation, parameter-estimation, tidyverse |
| Last editorial update | 50m ago | 1h ago |
| Website | Visit → | Visit → |
A young Mathematics Genealogy client spending its first four releases satisfying CRAN.
maths.genealogy queries the Mathematics Genealogy Project over a WebSocket connection and renders academic advisor-student trees, with plot_grviz() as the visualisation entry point. The package reached CRAN in early 2025 and its functional surface has barely moved since — max_zoom() for deep trees at 0.1.1 is the only user-facing addition in the visible history.
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
TidyDensity generates tidy-format random data from statistical distributions, with parameter estimation, AIC calculation, summary tables and automatic plotting for each one. Its releases follow a fixed template — breaking changes, new features, minor fixes — and the breaking section is usually empty. Growth comes distribution by distribution: Bernoulli, Burr, triangular, chi-square, zero-truncated negative binomial and others each arrive with a matching set of param_estimate, aic and stats_tbl helpers.
maths.genealogy queries the Mathematics Genealogy Project over a WebSocket connection and renders academic advisor-student trees, with plot_grviz() as the visualisation entry point. The package reached CRAN in early 2025 and its functional surface has barely moved since — max_zoom() for deep trees at 0.1.1 is the only user-facing addition in the visible history.
Every release after the first is CRAN policy management. Three consecutive entries deal with the same underlying problem: examples that hit a live network resource and therefore fail unpredictably on check machines. The progression from wrapping them in \donttest{} to catching a stray case to rewriting all examples against published API-package guidance shows the maintainer converging on a pattern rather than adding features. That is the normal cost of shipping a network client to CRAN, and it appears to be settling.
With the examples problem resolved, the next release is the first plausible opportunity for feature work — likely on the plotting side, given max_zoom() was the sole non-compliance change so far. The entries do not name anything specific in progress.
TidyDensity generates tidy-format random data from statistical distributions, with parameter estimation, AIC calculation, summary tables and automatic plotting for each one. Its releases follow a fixed template — breaking changes, new features, minor fixes — and the breaking section is usually empty. Growth comes distribution by distribution: Bernoulli, Burr, triangular, chi-square, zero-truncated negative binomial and others each arrive with a matching set of param_estimate, aic and stats_tbl helpers.
The package is filling out a matrix rather than changing shape — every new distribution gets the same four or five companion functions, so the surface grows predictably and the design does not. What variation exists comes from utilities that work across distributions: MCMC sampling, bootstrap helpers, time series conversion, distribution comparison. The two genuine breaking changes in this window were both internal reworks, moving generation onto data.table and rewriting quantile normalization for speed.
The established pattern of adding a distribution with its full helper set is the most likely continuation. Recent releases have been small, suggesting the catalogue is approaching the distributions its author considers worth covering.
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 maths.genealogy or TidyDensity.
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
See all maths.genealogy alternatives → · See all TidyDensity alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. maths.genealogy and TidyDensity 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. maths.genealogy and TidyDensity 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 Analytics products to evaluate alongside.
Top maths.genealogy alternatives in Analytics are ranked by recent ship velocity. Browse the "maths.genealogy alternatives" section above for the current picks, or visit /alternatives/maths-genealogy for the full list with editorial commentary on each.
Top TidyDensity alternatives in Analytics are ranked by recent ship velocity. Browse the "TidyDensity alternatives" section above for the current picks, or visit /alternatives/tidydensity for the full list with editorial commentary on each.