rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
gtsummary vs spatstat.model
A side-by-side editorial comparison of gtsummary and spatstat.model — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
gtsummary vs spatstat.model: at a glance
What is gtsummary?
gtsummary is quietly rebuilding itself around analysis results data, one table verb at a time.
gtsummary builds publication-ready summary, regression and survival tables for clinical and epidemiological work. Across this window it has grown in two directions at once: table composition primitives — splitting tables by rows and columns, stacking with labeled IDs, nested strata stacks, flexible merge columns — and a steadily deepening ARD layer, where tbl_ard_* functions, gather_ard() and the hierarchical table family expose the underlying analysis results data as a first-class object.
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.
gtsummary vs spatstat.model: editorial side-by-side
gtsummary
0.0ANALYTICS
gtsummary is quietly rebuilding itself around analysis results data, one table verb at a time.
◆ Current state
gtsummary builds publication-ready summary, regression and survival tables for clinical and epidemiological work. Across this window it has grown in two directions at once: table composition primitives — splitting tables by rows and columns, stacking with labeled IDs, nested strata stacks, flexible merge columns — and a steadily deepening ARD layer, where tbl_ard_* functions, gather_ard() and the hierarchical table family expose the underlying analysis results data as a first-class object.
◆ Where it's heading
The ARD work is the through-line. Table IDs exist so gather_ard() can return a named list; hierarchical tables gained per-level sorting and targeted filtering; ARD inputs are pre-processed so sorting applies to non-standard shapes. The package is becoming a structured-results engine that happens to render tables, rather than a renderer alone. Alongside that, 2.2.0 restored data pre-processing that 2.0 had removed after the reduced functionality hurt users — a maintainer willing to reverse a major-version decision.
◆ Prediction
Expect the hierarchical and ARD functions, introduced as a preview without a full deprecation cycle, to keep stabilizing toward a settled API rather than new table types appearing.
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 gtsummary 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 gtsummary 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 gtsummary alternatives → · See all spatstat.model alternatives →
Recent activity from gtsummary 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
- 2mo agogtsummaryTheme elements no longer evaluated by default
- 6mo agospatstat.modelComposite likelihood for cluster processes
- 8mo agogtsummaryARD strata functions and finer theme control
- 8mo agospatstat.modelReplicated network models and partial residuals
- 10mo agospatstat.modelintensity.ppm improvements for Geyer models
- 11mo agogtsummaryPer-level hierarchical sorting and labeled stacking
- 1y agospatstat.modelROC curve support substantially extended
- 1y agogtsummaryTable splitting, ID labeling, and add_difference_row
- 1y agogtsummaryData pre-processing restored after the 2.0 removal
- 1y agogtsummarytbl_merge gains explicit merge columns
Frequently asked questions
What is the difference between gtsummary 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 gtsummary 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 gtsummary?
Top gtsummary alternatives in Analytics are ranked by recent ship velocity. Browse the "gtsummary alternatives" section above for the current picks, or visit /alternatives/gtsummary-r 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.