qqman
The Manhattan-plot package for GWAS results, finished and dormant since 2017.
Comparison · Analytics
ggmagnify vs lpjmlkit
A side-by-side editorial comparison of ggmagnify and lpjmlkit — release velocity, themes, recent moves, and the top alternatives to consider.
ggmagnify vs lpjmlkit: at a glance
What is ggmagnify?
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.
What is lpjmlkit?
The LPJmL toolkit finally reads NetCDF, closing a gap against the format its field uses.
lpjmlkit is the R toolkit for running the LPJmL dynamic global vegetation model and reading its output. The 1.8.0 release adds direct NetCDF reading, either straight or via .nc.json metafiles, alongside the package's own binary formats. Before that, 1.7.3 sped up read_io(), reworked the LPJmLGridData implementation and added reservoir input support. Releases are infrequent, roughly one every 12 to 18 months.
ggmagnify vs lpjmlkit: editorial side-by-side
ggmagnify
0.0ANALYTICS
A single-purpose ggplot2 inset tool, refining the same three arguments.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
Too few entries to call a direction with confidence; continued small styling arguments would be consistent with what is visible.
lpjmlkit
0.0ANALYTICS
The LPJmL toolkit finally reads NetCDF, closing a gap against the format its field uses.
◆ Current state
lpjmlkit is the R toolkit for running the LPJmL dynamic global vegetation model and reading its output. The 1.8.0 release adds direct NetCDF reading, either straight or via .nc.json metafiles, alongside the package's own binary formats. Before that, 1.7.3 sped up read_io(), reworked the LPJmLGridData implementation and added reservoir input support. Releases are infrequent, roughly one every 12 to 18 months.
◆ Where it's heading
The work concentrates on the I/O layer rather than the modeling interface, and it is moving toward the formats the wider earth-system community already exchanges. The gap between 1.7.3 and 1.8.0 is over a year, so this is a research-group package released when the science requires it, not on a schedule. The changelog itself is thin — several entries are merge-commit text or CRAN resubmissions.
◆ Prediction
Further I/O breadth is the likeliest direction now that NetCDF is supported, though the entries give no schedule; the release cadence has not been regular enough to predict timing.
Alternatives to ggmagnify and lpjmlkit
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 lpjmlkit.
constants
The R package for CODATA constants rebuilt its symbol table on NIST's naming so future updates stop being hand work.
austraits
The R client for AusTraits spends its releases chasing the dataset it reads.
ggdemetra
A ggplot2 layer for seasonal adjustment output, filling in one plot type at a time.
fossilsim
A fossil-record simulator that quietly grew a trait-evolution engine.
tern.rbmi
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
See all ggmagnify alternatives → · See all lpjmlkit alternatives →
Recent activity from ggmagnify and lpjmlkit
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 5mo agolpjmlkitNetCDF and .nc.json metafile reading support
- 1y agolpjmlkitread_io() speedup and reservoir input support
- 2y agoggmagnifyFixes inset theme override on supplied plots
- 2y agoggmagnifyAdds fill between projection lines
- 2y agoggmagnifyAdds corner radius for target and inset
- 3y agolpjmlkitCRAN resubmission for Mac M1 build failures
- 3y agolpjmlkitCRAN resubmission for Mac M1 build failures
- 3y agolpjmlkit1.0.0 restructure introduces argument deprecations
- 3y agolpjmlkitData type naming and quote character fixes
Frequently asked questions
What is the difference between ggmagnify and lpjmlkit?
They serve adjacent needs but don't currently overlap on shipped themes. ggmagnify and lpjmlkit 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.
Is ggmagnify better than lpjmlkit?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggmagnify and lpjmlkit 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.
What are the best alternatives to ggmagnify?
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.
What are the best alternatives to lpjmlkit?
Top lpjmlkit alternatives in Analytics are ranked by recent ship velocity. Browse the "lpjmlkit alternatives" section above for the current picks, or visit /alternatives/lpjmlkit-r for the full list with editorial commentary on each.