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The Manhattan-plot package for GWAS results, finished and dormant since 2017.
A side-by-side editorial comparison of ggcorrplot and symengine — release velocity, themes, recent moves, and the top alternatives to consider.
ggcorrplot came back after four years and found its significance markers had been lying
ggcorrplot draws correlation matrices in ggplot2 with optional significance marking and hierarchical reordering. It sat untouched from late 2022 until mid-2026, then shipped 0.2.0 and 0.3.0 sixteen days apart. Between them they added the display options users had been requesting since 2016 and repaired a set of bugs where hc.order = TRUE silently changed which cells were marked significant.
An R symbolic-maths binding whose changelog is really the C++ core's release notes.
symengine gives R access to the SymEngine computer algebra core for symbolic expressions, matrices and sets. The tracked feed carries the upstream C++ library's releases rather than R-binding changes, so what shows here is core work: parser fixes, locale-independent double parsing, an SOVERSION bump and matrix transpose corrections. Feature growth in the core has slowed considerably since the 0.9 and 0.10 releases.
ggcorrplot draws correlation matrices in ggplot2 with optional significance marking and hierarchical reordering. It sat untouched from late 2022 until mid-2026, then shipped 0.2.0 and 0.3.0 sixteen days apart. Between them they added the display options users had been requesting since 2016 and repaired a set of bugs where hc.order = TRUE silently changed which cells were marked significant.
Both releases chase the same target: parity with the older corrplot package inside a ggplot2 object. Significance stars appended to coefficient labels, circle scaling, decimal control, then boxed cells and glyphs sized by absolute correlation — these are corrplot's visual vocabulary reimplemented where they can be composed with other ggplot2 layers. The bug fixes point the other way, at foundations: p-values matched to cells by name rather than row position, clustering computed on the unrounded matrix, tl.col actually applied.
With the corrplot look largely reproduced and the correctness backlog cleared, the remaining gap is the mixed upper/lower display corrplot supports; that is the natural next argument if the current release pace holds.
symengine gives R access to the SymEngine computer algebra core for symbolic expressions, matrices and sets. The tracked feed carries the upstream C++ library's releases rather than R-binding changes, so what shows here is core work: parser fixes, locale-independent double parsing, an SOVERSION bump and matrix transpose corrections. Feature growth in the core has slowed considerably since the 0.9 and 0.10 releases.
The upstream core has moved from adding capability — serialization, a first simplify(), set types, matrix expressions, LLVM support — toward maintenance: build fixes, dependency support such as Flint3, and correctness patches. For R users the practical consequence is that new symbolic features arrive only as fast as the binding exposes them, which this feed does not report on.
Expect further upstream maintenance releases tracking LLVM and Flint versions; nothing in these notes signals a new capability push.
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 ggcorrplot or symengine.
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 ggcorrplot alternatives → · See all symengine alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ggcorrplot is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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. ggcorrplot is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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 ggcorrplot alternatives in Analytics are ranked by recent ship velocity. Browse the "ggcorrplot alternatives" section above for the current picks, or visit /alternatives/ggcorrplot for the full list with editorial commentary on each.
Top symengine alternatives in Analytics are ranked by recent ship velocity. Browse the "symengine alternatives" section above for the current picks, or visit /alternatives/symengine-r for the full list with editorial commentary on each.