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Comparison · Analytics
clinify vs spatstat.model
A side-by-side editorial comparison of clinify and spatstat.model — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
clinify vs spatstat.model: at a glance
What is clinify?
Clinical-table typesetting for R, closing the gap between R output and regulatory Word documents
clinify renders clinical trial tables into Word documents through {officer}, aiming at the layout conventions regulatory submissions expect. 0.4.0 is the current CRAN release and folds in an unreleased 0.3.1. The recent work is almost entirely about header and spacing control: which adjacent header cells merge, where the rule under a spanner starts and stops, and how much vertical space sits above, below and between header rows and the table body.
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.
clinify vs spatstat.model: editorial side-by-side
clinify
2.5ANALYTICS
Clinical-table typesetting for R, closing the gap between R output and regulatory Word documents
◆ Current state
clinify renders clinical trial tables into Word documents through {officer}, aiming at the layout conventions regulatory submissions expect. 0.4.0 is the current CRAN release and folds in an unreleased 0.3.1. The recent work is almost entirely about header and spacing control: which adjacent header cells merge, where the rule under a spanner starts and stops, and how much vertical space sits above, below and between header rows and the table body.
◆ Where it's heading
The package is moving from producing a correct table toward producing one that survives an organisation's house style. 0.4.0's additions are all written to hold under a customised `clinify_table_default()` — `clin_spanner_rule()` draws after the default styling function so it persists when a house style clears the borders it started from, and takes an `officer::fp_border()` or `FALSE` so a style can decline the rule entirely. The earlier 0.3.0 line did the structural work, introducing `clindoc()` document objects that accept multiple tables plus automatic pagination.
◆ Prediction
Expect continued refinement of layout primitives that follow the table rather than fixed column numbers, since both new 0.4.0 functions were built specifically to track spanners and headers as a layout changes. The entries do not indicate a move beyond Word output.
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 clinify 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 clinify or spatstat.model.
kernelshap
kernelshap makes permutation SHAP practical past eight features, then fixes the kernel weights it had wrong.
filtro
filtro moves to S7 and multiplies its feature-scoring methods in a single release.
modeltime.resample
modeltime.resample exists to keep backtesting working as tidymodels shifts underneath it.
modeltime.ensemble
modeltime.ensemble wakes after four years, and the work is all tune 2.0 compatibility.
shapviz
shapviz refines its SHAP plots release by release while chasing ggplot2's moving target.
See all clinify alternatives → · See all spatstat.model alternatives →
Recent activity from clinify and spatstat.model
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 13d agoclinifyFiner header merging, spanner rules and header padding
- 18d agospatstat.modelVariance-covariance and diagnostics for determinantal models
- 2mo agospatstat.modelMore intensity and repul methods; boundary-aware predictions
- 6mo agospatstat.modelComposite likelihood for cluster processes
- 8mo agospatstat.modelReplicated network models and partial residuals
- 10mo agospatstat.modelintensity.ppm improvements for Geyer models
- 1y agospatstat.modelROC curve support substantially extended
- 1y agoclinifyclindoc document objects and automatic pagination
Frequently asked questions
What is the difference between clinify and spatstat.model?
Both compete on the same themes — r-package — within Analytics. clinify and spatstat.model are shipping at a similar cadence (velocity 2.5 vs 2.5, 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 clinify better than spatstat.model?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. clinify and spatstat.model are shipping at a similar cadence (velocity 2.5 vs 2.5, 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 clinify?
Top clinify alternatives in Analytics are ranked by recent ship velocity. Browse the "clinify alternatives" section above for the current picks, or visit /alternatives/clinify-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.