RMVMR
RMVMR is being tidied in lockstep with MVMR, the package it wraps
A side-by-side editorial comparison of spatstat.random and treeshap — release velocity, themes, recent moves, and the top alternatives to consider.
spatstat's simulation engine pushes point process generation into three dimensions
spatstat.random generates random point patterns and simulates point process models for the spatstat family. Its recent releases have moved along two lines at once: filling out three-dimensional simulation, and adding conditional simulation to the established cluster process generators. 3.5-1 is a narrow follow-up adding a random Dirichlet-Voronoi tessellation without edge effects.
treeshap keeps widening its tree-model coverage while the SHAP math stays put.
treeshap computes exact SHAP values for tree ensembles in R, reaching each modelling framework through a per-framework unify() adapter. Since returning to CRAN in 2023 it has added GPBoost, ranger survival forests and multi-output models to that adapter layer. Four releases in three years, each dominated by adapter work contributed by users of one specific framework.
spatstat.random generates random point patterns and simulates point process models for the spatstat family. Its recent releases have moved along two lines at once: filling out three-dimensional simulation, and adding conditional simulation to the established cluster process generators. 3.5-1 is a narrow follow-up adding a random Dirichlet-Voronoi tessellation without edge effects.
The clearest arc is dimensional. 3.5-0 carried inhomogeneous Poisson processes, non-uniform random points and Simple Sequential Inhibition into 3D in a single release, and the sibling geometry package followed two months later with more capabilities for three-dimensional point patterns. Alongside that, the generators have been gaining theoretical range — Gaussian random fields in 3.4-4, a new class of theoretical cluster process models and random diffusion in 3.5-0 — while earlier releases concentrated on conditional simulation and efficiency in the existing 2D routines.
Expect the 3D work to continue propagating into the model-fitting and geometry packages before spatstat.random adds another dimension-independent generator, since the 3D features here have already begun appearing downstream. The entries do not indicate which estimator gets 3D support next.
treeshap computes exact SHAP values for tree ensembles in R, reaching each modelling framework through a per-framework unify() adapter. Since returning to CRAN in 2023 it has added GPBoost, ranger survival forests and multi-output models to that adapter layer. Four releases in three years, each dominated by adapter work contributed by users of one specific framework.
The direction is breadth of model support rather than new explanation methods: every release since the first CRAN submission adds or repairs a unify() backend. Maintenance is community-driven, with named contributors fixing the framework they personally use. Nothing in these entries points at work on the SHAP algorithms themselves.
Expect the next release to add or repair another unify() adapter as a contributor brings their own framework, rather than to change how explanations are computed.
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 spatstat.random or treeshap.
RMVMR is being tidied in lockstep with MVMR, the package it wraps
geoarrow tracks the GeoArrow spec and otherwise just keeps compiling
n2khab keeps retracting interpretations of habitat data it can't actually support
tidypolars is grinding toward complete dplyr coverage, one supported function at a time
OneSampleMR found that argument order in a formula was silently changing its estimates
bpbounds found the same swapped-cell bug twice and clamped its bounds back into range
See all spatstat.random alternatives → · See all treeshap alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. spatstat.random 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. spatstat.random 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 spatstat.random alternatives in Analytics are ranked by recent ship velocity. Browse the "spatstat.random alternatives" section above for the current picks, or visit /alternatives/spatstat-random for the full list with editorial commentary on each.
Top treeshap alternatives in Analytics are ranked by recent ship velocity. Browse the "treeshap alternatives" section above for the current picks, or visit /alternatives/treeshap for the full list with editorial commentary on each.