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Comparison · Analytics
bpbounds vs ggcorrplot
A side-by-side editorial comparison of bpbounds and ggcorrplot — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r
bpbounds vs ggcorrplot: at a glance
What is bpbounds?
bpbounds found the same swapped-cell bug twice and clamped its bounds back into range
bpbounds computes nonparametric Balke-Pearl bounds on the average causal effect from instrumental variable data, in the bivariate and trivariate cases. After years of pure packaging maintenance, the two 2026 releases are analytical corrections. Bounds on intervention probabilities are now clamped to [0, 1] so derived causal risk ratio bounds cannot fall outside their feasible range, and a cell-ordering error in the trivariate three-category instrument path has been repaired.
What is ggcorrplot?
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.
bpbounds vs ggcorrplot: editorial side-by-side
bpbounds
0.0ANALYTICS
bpbounds found the same swapped-cell bug twice and clamped its bounds back into range
◆ Current state
bpbounds computes nonparametric Balke-Pearl bounds on the average causal effect from instrumental variable data, in the bivariate and trivariate cases. After years of pure packaging maintenance, the two 2026 releases are analytical corrections. Bounds on intervention probabilities are now clamped to [0, 1] so derived causal risk ratio bounds cannot fall outside their feasible range, and a cell-ordering error in the trivariate three-category instrument path has been repaired.
◆ Where it's heading
The direction is toward agreement with the reference Stata implementation and away from silently wrong output. The clamping change is described as matching the same fix in the Stata package, which suggests the two implementations are being reconciled rather than developed independently. The cell-ordering defect is the more instructive one: it was fixed in the calculation function in 0.1.7 and then again in the constraint matrix in 0.1.8, meaning the same x=0,y=1 / x=1,y=0 swap had been written in two places.
◆ Prediction
Since the recent fixes came from an external contributor's report and both touched the trivariate three-category path, the untested corners of that path are where further corrections would surface — but the release notes give no roadmap beyond parity with the Stata package.
ggcorrplot
2.5ANALYTICS
ggcorrplot came back after four years and found its significance markers had been lying
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
Alternatives to bpbounds and ggcorrplot
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 bpbounds or ggcorrplot.
fastplyr
A fast dplyr stand-in that keeps finding new places to skip work entirely.
trias
Belgium's invasive-species indicator toolkit is in steady refinement, one plotting edge case at a time.
icesSAG
The ICES stock assessment client took upload away in 2024 and spent two years giving it back.
dggridR
A discrete global grid generator grew cell traversal and became a usable spatial index.
vegan
Community ecology's standard toolkit is retiring the functions a generation of scripts was built on.
See all bpbounds alternatives → · See all ggcorrplot alternatives →
Recent activity from bpbounds and ggcorrplot
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 21d agoggcorrplotggcorrplot 0.3.0 adds boxed cells and correlation-sized squares
- 1mo agobpboundsbpbounds clamps probability bounds and fixes a constraint-matrix swap
- 1mo agoggcorrplotggcorrplot 0.2.0 fixes significance markers broken by hc.order
- 2mo agobpboundsbpbounds fixes swapped cells in the trivariate calculation
- 2y agobpboundsbpbounds 0.1.6
- 3y agobpboundsbpbounds 0.1.5
- 3y agoggcorrplotggcorrplot 0.1.4
- 6y agobpboundsVersion 0.1.4 on CRAN
- 6y agoggcorrplotggcorrplot 0.1.3
- 7y agoggcorrplotggcorrplot 0.1.2
- 7y agobpboundsVersion 0.1.3
- 10y agoggcorrplotggcorrplot's first release: correlograms in ggplot2
Frequently asked questions
What is the difference between bpbounds and ggcorrplot?
Both compete on the same themes — r — within Analytics. 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.
Is bpbounds better than ggcorrplot?
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.
What are the best alternatives to bpbounds?
Top bpbounds alternatives in Analytics are ranked by recent ship velocity. Browse the "bpbounds alternatives" section above for the current picks, or visit /alternatives/bpbounds for the full list with editorial commentary on each.
What are the best alternatives to ggcorrplot?
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.