mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of ggpointless and netdiffuseR — release velocity, themes, recent moves, and the top alternatives to consider.
ggpointless keeps adding the ggplot2 layers nobody else bothered to write.
ggpointless is a small ggplot2 extension collecting geoms that sit outside the standard set — Lexis diagrams, Chaikin-smoothed paths, hanging chains, Fourier reconstructions — and it has grown steadily rather than changed shape. The May release is the largest yet: isotype and pictogram bar charts as stacks of discrete unit cells, a family of geoms that fade paths, segments, curves and reference lines along their length, and geom_gridline(), which draws grid lines as a layer on top of the data rather than beneath it.
Network diffusion analysis returns from a seven-year gap able to track several behaviours at once
netdiffuseR analyses how behaviours spread through networks — exposure, adoption timing, thresholds, and simulation of diffusion processes. Its feed has a hole: four releases from 2024 to 2026 sit directly on top of three from 2016 and 2017, with the intervening versions absent. The current line is being maintained by a widening group of contributors and, at 1.24.0, was explicitly brought back to CRAN.
ggpointless is a small ggplot2 extension collecting geoms that sit outside the standard set — Lexis diagrams, Chaikin-smoothed paths, hanging chains, Fourier reconstructions — and it has grown steadily rather than changed shape. The May release is the largest yet: isotype and pictogram bar charts as stacks of discrete unit cells, a family of geoms that fade paths, segments, curves and reference lines along their length, and geom_gridline(), which draws grid lines as a layer on top of the data rather than beneath it.
Two patterns are visible. Ideas get generalized rather than left as one-offs: geom_area_fade() in the previous release established alpha gradients via grid::linearGradient(), and the recent release spreads that treatment across paths, lines, steps, segments, curves and the three reference-line geoms, each with the same fade_direction and alpha_fade_to arguments. And each new geom is expected to survive real plots — the unit charts work under coord_equal, coord_polar, coord_radial, coord_flip and faceting, and geom_gridline reads positions from trained scales and inherits styling from the theme's panel grid. The package also tracks ggplot2 closely, requiring 4.0.0 and using make_constructor() and gg_par() internally, and it dropped its bundled datasets outright rather than maintain stale copies.
The fade treatment now covers most path-like geoms but not the area and ribbon family beyond geom_area_fade(), which is where the pattern has room left to run. The unit-cell charts arrive with a label helper and no fill or grouping variants, so those are the plausible next additions.
netdiffuseR analyses how behaviours spread through networks — exposure, adoption timing, thresholds, and simulation of diffusion processes. Its feed has a hole: four releases from 2024 to 2026 sit directly on top of three from 2016 and 2017, with the intervening versions absent. The current line is being maintained by a widening group of contributors and, at 1.24.0, was explicitly brought back to CRAN.
The recent releases read as institutional rather than exploratory: CI fixes, CRAN-readiness passes, contributed PRs from new names, bundled teaching datasets. The one structural move is 1.23.0, named for multi-adoption, which alongside a refactor of the exposure and rdiffnet internals adds a function for splitting behaviours apart — the package handling several diffusing behaviours where its object model previously carried one.
With CRAN presence restored and a dataset for a teaching game added in the newest release, the near-term direction looks like classroom and workshop use rather than new method surface. Whether multi-adoption gets its own analysis functions, rather than a splitter, is the open question these notes do not answer.
Other Infra & APIs 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 ggpointless or netdiffuseR.
mice can finally predict, not just estimate, from multiply imputed data.
A market-microstructure toolkit that keeps adding estimators as the papers land.
A vowel-analysis package trimming dependencies after an email address got it archived.
The R half of the EMU speech database system, fixing what was quietly broken.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
tidyplots keeps rebuilding its own foundations rather than layering around them.
See all ggpointless alternatives → · See all netdiffuseR alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ggpointless and netdiffuseR are shipping at a similar cadence (velocity 0.0 vs 0.0, 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggpointless and netdiffuseR are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
Top ggpointless alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ggpointless alternatives" section above for the current picks, or visit /alternatives/ggpointless for the full list with editorial commentary on each.
Top netdiffuseR alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "netdiffuseR alternatives" section above for the current picks, or visit /alternatives/netdiffuser for the full list with editorial commentary on each.