rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
ggstatsplot vs spatstat.model
A side-by-side editorial comparison of ggstatsplot and spatstat.model — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
ggstatsplot vs spatstat.model: at a glance
What is ggstatsplot?
ggstatsplot reached 1.0 by adding tests, having outsourced its statistics years ago.
ggstatsplot produces ggplot2 graphics with statistical test results embedded in the subtitle and caption — comparisons, correlations, contingency tables, histograms. Since the 2019 refactoring that moved all statistical computation into the separate statsExpressions package, its own release notes have been dominated by upstream tracking: adapting to ggplot2, dplyr, purrr and easystats changes. The 1.0.0 release in April 2026 breaks that run with real additions to the contingency-table functions.
What is spatstat.model?
spatstat's inference layer builds out determinantal and cluster process fitting
spatstat.model fits point process models and provides the diagnostics that go with them. The recent window is dominated by determinantal point process work — a variance-covariance matrix and more diagnostics in 3.7-2, additional `intensity` and `repul` methods in 3.7-1, and ROC curves for determinantal models in 3.5-0. Cluster and Cox process inference has advanced in parallel, with Waagepetersen's composite likelihood arriving in 3.6-1.
ggstatsplot vs spatstat.model: editorial side-by-side
ggstatsplot
0.0ANALYTICS
ggstatsplot reached 1.0 by adding tests, having outsourced its statistics years ago.
◆ Current state
ggstatsplot produces ggplot2 graphics with statistical test results embedded in the subtitle and caption — comparisons, correlations, contingency tables, histograms. Since the 2019 refactoring that moved all statistical computation into the separate statsExpressions package, its own release notes have been dominated by upstream tracking: adapting to ggplot2, dplyr, purrr and easystats changes. The 1.0.0 release in April 2026 breaks that run with real additions to the contingency-table functions.
◆ Where it's heading
The architecture explains the cadence. With statistics living in statsExpressions, ggstatsplot's own releases are mostly the tax of sitting on top of a fast-moving plotting and tidyverse stack — five of the six most recent entries change nothing a user would notice. When substantive work does arrive it clusters in the plotting layer's coverage of test families, as in 1.0.0's one-sample goodness-of-fit support and pairwise contingency analyses. The maintainer is also visibly deliberate about scope, having removed the normality-curve overlay in 0.12.4 for being unrelated to the analysis in question.
◆ Prediction
Expect continued parity work across the plot family — features that exist in one function being extended to its siblings, as goodness-of-fit support moved from ggpiestats to ggbarstats — punctuated by maintenance releases tracking ggplot2 and easystats.
spatstat.model
2.5ANALYTICS
spatstat's inference layer builds out determinantal and cluster process fitting
◆ Current state
spatstat.model fits point process models and provides the diagnostics that go with them. The recent window is dominated by determinantal point process work — a variance-covariance matrix and more diagnostics in 3.7-2, additional `intensity` and `repul` methods in 3.7-1, and ROC curves for determinantal models in 3.5-0. Cluster and Cox process inference has advanced in parallel, with Waagepetersen's composite likelihood arriving in 3.6-1.
◆ Where it's heading
The pattern is that model classes enter the package as fitting machinery first and only later gain the apparatus that makes them usable in practice — standard errors, diagnostics, residuals, model checking. Determinantal processes are visibly midway through that progression, reaching variance-covariance estimation only in the most recent release. Around this, the package has been broadening where models can be fitted at all: replicated point patterns on linear networks in 3.5-0, extended spatial logistic regression, and conversion of recursively partitioned models to tessellations.
◆ Prediction
Expect determinantal model support to keep filling out along the same path other model classes took, since variance estimation has only just arrived and partial residuals already exist for the cluster and Cox families. The entries do not signal a move into three dimensions here, unlike the geometry and simulation packages.
Alternatives to ggstatsplot and spatstat.model
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 ggstatsplot or spatstat.model.
audubon
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
affiner
affiner is quietly turning a grid transformation helper into a small computational geometry library.
ageproR
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
r-ledger
ledger adds a Rust toolchain fallback, so beancount imports work whether or not the Python tooling is installed.
gridpattern
gridpattern keeps widening its catalogue, and the newest patterns finally use the device's own line rendering.
See all ggstatsplot alternatives → · See all spatstat.model alternatives →
Recent activity from ggstatsplot and spatstat.model
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 18d agospatstat.modelVariance-covariance and diagnostics for determinantal models
- 2mo agospatstat.modelMore intensity and repul methods; boundary-aware predictions
- 3mo agoggstatsplotPairwise contingency tests and one-sample goodness-of-fit
- 4mo agoggstatsplotInternal maintenance only
- 6mo agoggstatsplotAdapted to dplyr 1.2.0 and purrr 1.2.1
- 6mo agospatstat.modelComposite likelihood for cluster processes
- 8mo agoggstatsplotContributor list updated in DESCRIPTION
- 8mo agospatstat.modelReplicated network models and partial residuals
- 10mo agoggstatsplotSecondary axis label parsing fixed in gghistostats
- 10mo agospatstat.modelintensity.ppm improvements for Geyer models
- 11mo agoggstatsplotAdapted to the latest ggplot2 release
- 1y agospatstat.modelROC curve support substantially extended
Frequently asked questions
What is the difference between ggstatsplot and spatstat.model?
Both compete on the same themes — r-package — within Analytics. spatstat.model 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 ggstatsplot better than spatstat.model?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. spatstat.model 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 ggstatsplot?
Top ggstatsplot alternatives in Analytics are ranked by recent ship velocity. Browse the "ggstatsplot alternatives" section above for the current picks, or visit /alternatives/ggstatsplot for the full list with editorial commentary on each.
What are the best alternatives to spatstat.model?
Top spatstat.model alternatives in Analytics are ranked by recent ship velocity. Browse the "spatstat.model alternatives" section above for the current picks, or visit /alternatives/spatstat-model for the full list with editorial commentary on each.