STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of healthyR.ts and TidyDensity — release velocity, themes, recent moves, and the top alternatives to consider.
healthyR.ts keeps adding time-series helpers, then quietly breaks the old ones to modernise them.
A time-series companion in the healthyverse family, shipping helper functions in batches: growth-rate vectors, an ADF test and auto_stationarize() in 0.2.11, then five log and differencing transforms in 0.3.0, and a random-walk plot in 0.3.2. Alongside the additions runs a steady stream of breaking cleanups — invisible returns dropped, R 4.1 required for the native pipe, and ts_ma_plot() refactored onto ggplot2 facets with its xts output removed and its return value cut from six items to two.
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.
A time-series companion in the healthyverse family, shipping helper functions in batches: growth-rate vectors, an ADF test and auto_stationarize() in 0.2.11, then five log and differencing transforms in 0.3.0, and a random-walk plot in 0.3.2. Alongside the additions runs a steady stream of breaking cleanups — invisible returns dropped, R 4.1 required for the native pipe, and ts_ma_plot() refactored onto ggplot2 facets with its xts output removed and its return value cut from six items to two.
Two threads, both consistent. The functional one is coverage of the stationarity workflow — transform, test, auto-stationarize, plot — assembled function by function rather than as a single API. The structural one is convergence on ggplot2 and tidy conventions, retiring xts objects and multi-object return lists as it goes. The package is not afraid to break return shapes to get there, so upgrades are not drop-in.
Expect the remaining functions that still return xts objects or bundled lists to get the same ggplot2-only treatment, since ts_ma_plot() was refactored on exactly that rationale.
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 healthyR.ts 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 healthyR.ts alternatives → · See all TidyDensity alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. healthyR.ts 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. healthyR.ts 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 healthyR.ts alternatives in Analytics are ranked by recent ship velocity. Browse the "healthyR.ts alternatives" section above for the current picks, or visit /alternatives/healthyr-ts 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.