compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of tidycmprsk and topocast — release velocity, themes, recent moves, and the top alternatives to consider.
Competing-risks modelling that now moves only when its neighbours do.
tidycmprsk wraps competing risks regression and cumulative incidence estimation in tidy-style output, so results slot into gtsummary tables and ggsurvfit plots. The last two releases are small: 1.1.2 sorts tidy.tidycuminc() output by stratum, 1.1.1 is an HTML5 documentation update for CRAN. The substantive work in the window is 1.1.0, which reorganised the gtsummary relationship.
New R package downscaling coarse climate rasters onto fine terrain, now five times cheaper per call.
topocast is a young R package — first released in June 2026 — that downscales coarse rasters onto fine terrain using moving-window regression, with the relationship expressed as a formula over layer names. Its defining implementation choice is summed-area tables, which make the cost of estimating coefficient grids independent of the window radius. Three releases in six weeks have taken it from first publication through real-workflow fixes to a substantial performance restructuring.
tidycmprsk wraps competing risks regression and cumulative incidence estimation in tidy-style output, so results slot into gtsummary tables and ggsurvfit plots. The last two releases are small: 1.1.2 sorts tidy.tidycuminc() output by stratum, 1.1.1 is an HTML5 documentation update for CRAN. The substantive work in the window is 1.1.0, which reorganised the gtsummary relationship.
The package has spent its releases handing responsibilities to neighbouring packages rather than growing its own surface. Plotting was deprecated then made defunct in favour of ggsurvfit::ggcuminc(), and 1.1.0 moved the regression table methods so that gtsummary could drop tidycmprsk as a dependency. What remains is the estimation core plus the S3 methods that let other packages consume it, which is a deliberate narrowing.
Expect releases to continue tracking changes in gtsummary and the broader tidy survival stack rather than adding estimation features.
topocast is a young R package — first released in June 2026 — that downscales coarse rasters onto fine terrain using moving-window regression, with the relationship expressed as a formula over layer names. Its defining implementation choice is summed-area tables, which make the cost of estimating coefficient grids independent of the window radius. Three releases in six weeks have taken it from first publication through real-workflow fixes to a substantial performance restructuring.
Development is being driven by running the package against real datasets — the second release names CHELSA and SRTM as the source of its three fixes — and the third is a direct response to multi-response calls repeating work. The arc is the ordinary one for a new method package: publish the method, then discover that real inputs have more responses, more coordinate-system edge cases, and more repeated structure than the initial design assumed. Coefficient grids being exposed as output suggests the local regression parameters, such as lapse rate, are as interesting to users as the downscaled values.
Expect continued work on multi-response and time-series throughput, and more coordinate-system and input-validation handling as the package meets further real climate datasets.
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 tidycmprsk or topocast.
An MLE package rebuilt around composable solvers, then renamed to match.
nabla dropped its C++ engine to chase exact derivatives at any order.
Eight months from first release to keyring caching and workload identity.
A research-project workflow package where the interesting work is in the plumbing.
A cyclomatic complexity checker that ships once every couple of years, and lands when it does.
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
See all tidycmprsk alternatives → · See all topocast alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. topocast 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. topocast 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 tidycmprsk alternatives in Analytics are ranked by recent ship velocity. Browse the "tidycmprsk alternatives" section above for the current picks, or visit /alternatives/tidycmprsk for the full list with editorial commentary on each.
Top topocast alternatives in Analytics are ranked by recent ship velocity. Browse the "topocast alternatives" section above for the current picks, or visit /alternatives/topocast for the full list with editorial commentary on each.