HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
A side-by-side editorial comparison of daedalus and driveR — release velocity, themes, recent moves, and the top alternatives to consider.
An epidemic-economic model teaching its interventions to react to the outbreak itself.
daedalus couples an infectious-disease model to sector-level economic costs, letting users test non-pharmaceutical interventions against both epidemic and fiscal outcomes. Over autumn 2025 the intervention layer went from a single fixed closure to sequential timed NPIs and then to closures that lift in response to the model's own instantaneous reproduction number. Alongside that, the cost functions were reworked so illness states map more carefully onto lost productivity.
A cancer driver prioritization package that ships rarely and mostly to stay installable
driveR prioritizes cancer driver genes from somatic variant and copy number data, combining coding impact scores, noncoding impact, copy number alteration scores and hotspot annotations into a multi-task learning classification model. Version 0.5.0 added gene-level SCNA data frames as an accepted input to create_features_df(), with an example table shipped alongside, widening the entry point beyond the segment-level format. The same release moved org.Hs.eg.db and both hg19 and hg38 TxDb annotation packages from Imports to Suggests under new CRAN policy, with dependent functions now raising an error when they are absent rather than silently degrading.
daedalus couples an infectious-disease model to sector-level economic costs, letting users test non-pharmaceutical interventions against both epidemic and fiscal outcomes. Over autumn 2025 the intervention layer went from a single fixed closure to sequential timed NPIs and then to closures that lift in response to the model's own instantaneous reproduction number. Alongside that, the cost functions were reworked so illness states map more carefully onto lost productivity.
The arc is toward a model that behaves like a policy simulator rather than a scenario calculator: interventions now have their own state machine, R_t is computed inside the ODE system, and event handling has been pulled out of the output object. Correction releases sit between the feature ones, including an indexing fix the maintainers flag as required for accurate projections. The versioning is patch-level but the changes are structural.
With R_t and the next-generation matrix now available in-model, the likely next step is richer response rules keyed to those quantities; the entries give no signal on when a stable 1.0 arrives.
driveR prioritizes cancer driver genes from somatic variant and copy number data, combining coding impact scores, noncoding impact, copy number alteration scores and hotspot annotations into a multi-task learning classification model. Version 0.5.0 added gene-level SCNA data frames as an accepted input to create_features_df(), with an example table shipped alongside, widening the entry point beyond the segment-level format. The same release moved org.Hs.eg.db and both hg19 and hg38 TxDb annotation packages from Imports to Suggests under new CRAN policy, with dependent functions now raising an error when they are absent rather than silently degrading.
Releases are infrequent and split cleanly between capability and correction. GRCh38 support arrived in 0.4.0 and cancer-type-specific thresholds were refreshed in 0.3.0, while the 0.2.x pair fixed scoring errors serious enough to require retraining: a column name mismatch meant the SCNA score was not being computed at all, and MCR table coordinates needed converting from hg18 to hg19. Both times the bundled classification model and thresholds were rebuilt as a consequence. Since 0.4.0 the changes have been input handling and packaging rather than method.
The move of the annotation databases to Suggests suggests a leaner install is the current priority; the entries give no indication of planned model or scoring changes.
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 daedalus or driveR.
France's national flood statistics, ported out of Fortran and into R.
Sign, zero and narrative restrictions brought into the bsvars ecosystem.
Fast design-based estimators for experiments, coasting on CRAN patches.
The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.
IP address vectors for R that hit 1.0 and then went quiet.
A column-key toolkit for stitching decades of ecological field data into one table.
See all daedalus alternatives → · See all driveR alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. daedalus and driveR 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. daedalus and driveR 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 daedalus alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "daedalus alternatives" section above for the current picks, or visit /alternatives/daedalus for the full list with editorial commentary on each.
Top driveR alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "driveR alternatives" section above for the current picks, or visit /alternatives/driver for the full list with editorial commentary on each.