HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
A side-by-side editorial comparison of driveR and mize — release velocity, themes, recent moves, and the top alternatives to consider.
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.
A numerical optimization toolkit that has been feature-complete and quiet since 2017
mize provides a configurable interface to unconstrained numerical optimization methods - line searches, gradient descent variants, quasi-Newton updates - usable both as a one-shot call and as a stepwise iterator. Its functional surface has not changed since the initial CRAN release in July 2017. Every release since has been a patch: R-devel compatibility, line search edge cases, and in 2026's 0.2.5 the removal of LazyData from DESCRIPTION plus deletion of some flaky tests.
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.
mize provides a configurable interface to unconstrained numerical optimization methods - line searches, gradient descent variants, quasi-Newton updates - usable both as a one-shot call and as a stepwise iterator. Its functional surface has not changed since the initial CRAN release in July 2017. Every release since has been a patch: R-devel compatibility, line search edge cases, and in 2026's 0.2.5 the removal of LazyData from DESCRIPTION plus deletion of some flaky tests.
The pattern is a stable library rather than an abandoned one. Fixes address real reports - a bracket_step error when the Schmidt line search exhausts its function evaluation budget, an incorrect gradient count under backtracking with a specified step_down - and the maintainer used one release note to clarify that backtracking behaviour differs depending on whether step_down is supplied, which reads as answering a recurring question. The five and a half year gap between 0.2.4 and 0.2.5 is the clearest signal: the package is maintained on demand, not developed.
Expect nothing until an R or CRAN policy change forces another compliance patch, which is what triggered the most recent release.
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 driveR or mize.
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.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. driveR and mize 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. driveR and mize 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 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.
Top mize alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "mize alternatives" section above for the current picks, or visit /alternatives/mize for the full list with editorial commentary on each.