qqman
The Manhattan-plot package for GWAS results, finished and dormant since 2017.
A side-by-side editorial comparison of ggmagnify and redist — release velocity, themes, recent moves, and the top alternatives to consider.
A single-purpose ggplot2 inset tool, refining the same three arguments.
ggmagnify draws magnified insets of a region of a ggplot, with projection lines connecting the inset to its source area. The visible releases are all small refinements to how that inset looks — corner radius, fill between projection lines — plus one fix for inset themes being overridden. There are only three entries, so the picture is necessarily partial.
redist keeps rewriting the sampler underneath a district-drawing API it has held stable since 4.0.
redist simulates redistricting plans via sequential Monte Carlo, merge-split MCMC and short-burst optimization, and it is the analysis tool behind a good deal of published districting work. The user-facing shape was set by 4.0.1's constraint interface and the split of metrics into the redistmetrics package; since then the changes are in the algorithms. The most consequential recent one replaces the SMC label-counting adjustment with a backward kernel that removes approximation error outright.
ggmagnify draws magnified insets of a region of a ggplot, with projection lines connecting the inset to its source area. The visible releases are all small refinements to how that inset looks — corner radius, fill between projection lines — plus one fix for inset themes being overridden. There are only three entries, so the picture is necessarily partial.
Work concentrates on the visual finish of the inset rather than on new capability, which is what a package with one job should look like. Two feature releases a week apart in early 2024 suggest a short burst of attention rather than sustained development, and the feed goes quiet after mid-2024.
Too few entries to call a direction with confidence; continued small styling arguments would be consistent with what is visible.
redist simulates redistricting plans via sequential Monte Carlo, merge-split MCMC and short-burst optimization, and it is the analysis tool behind a good deal of published districting work. The user-facing shape was set by 4.0.1's constraint interface and the split of metrics into the redistmetrics package; since then the changes are in the algorithms. The most consequential recent one replaces the SMC label-counting adjustment with a backward kernel that removes approximation error outright.
The direction is toward exactness and throughput at once — the new kernel is described as both eliminating approximation error and costing far less computation, and successive releases keep adding parallelism, most recently to the flip algorithm. Feature growth has moved into the optimization side, where short-burst gained multiple independent scorers and a Pareto frontier. The release notes are not a reliable ledger: 4.3.1 ships the identical text as 4.3.0.
Expect the remaining single-threaded algorithms to gain the chains-style parallelism that flip just received, following the pattern SMC established several releases ago.
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 ggmagnify or redist.
The Manhattan-plot package for GWAS results, finished and dormant since 2017.
The R package for CODATA constants rebuilt its symbol table on NIST's naming so future updates stop being hand work.
The R client for AusTraits spends its releases chasing the dataset it reads.
A ggplot2 layer for seasonal adjustment output, filling in one plot type at a time.
A fossil-record simulator that quietly grew a trait-evolution engine.
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
See all ggmagnify alternatives → · See all redist alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ggmagnify and redist 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. ggmagnify and redist 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 ggmagnify alternatives in Analytics are ranked by recent ship velocity. Browse the "ggmagnify alternatives" section above for the current picks, or visit /alternatives/ggmagnify for the full list with editorial commentary on each.
Top redist alternatives in Analytics are ranked by recent ship velocity. Browse the "redist alternatives" section above for the current picks, or visit /alternatives/redist-r for the full list with editorial commentary on each.