HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
Comparison · Infra & APIs
driveR vs offsetreg
A side-by-side editorial comparison of driveR and offsetreg — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-packagemachine-learning
driveR vs offsetreg: at a glance
What is driveR?
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.
What is offsetreg?
The parsnip extension for exposure models grew from one algorithm to three
offsetreg supplies parsnip model specifications for regressions with offsets, the form actuarial and epidemiological work needs when modelling rates over exposure. It launched with a single specification, poisson_reg_offset(), backed by glm and glmnet engines. Version 1.1.0 added two more model types - boost_tree_offset() for boosted trees via xgboost and decision_tree_exposure() for weighted decision trees via rpart - and 1.2.0 has been consolidation: argument passthrough to the underlying glm and glmnet calls, an xgboost 3.0 minimum with API accommodation, and cli-formatted messages.
driveR vs offsetreg: editorial side-by-side
driveR
0.0INFRA · APIS
A cancer driver prioritization package that ships rarely and mostly to stay installable
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
offsetreg
0.0INFRA · APIS
The parsnip extension for exposure models grew from one algorithm to three
◆ Current state
offsetreg supplies parsnip model specifications for regressions with offsets, the form actuarial and epidemiological work needs when modelling rates over exposure. It launched with a single specification, poisson_reg_offset(), backed by glm and glmnet engines. Version 1.1.0 added two more model types - boost_tree_offset() for boosted trees via xgboost and decision_tree_exposure() for weighted decision trees via rpart - and 1.2.0 has been consolidation: argument passthrough to the underlying glm and glmnet calls, an xgboost 3.0 minimum with API accommodation, and cli-formatted messages.
◆ Where it's heading
The package is following the tidymodels extension playbook: establish one model type, then add types rather than engines, and keep pace with parsnip's own releases. Each version has also tightened the guardrails around correct use - a vignette on when offsetreg should and should not be used, check_args() methods on the specifications - which suggests the maintainer is fielding misapplication rather than feature requests.
◆ Prediction
Expect additional engines under the existing model types, or a fourth specification, before any change to the offset handling itself, since the package's structure invites extension at the engine layer.
Alternatives to driveR and offsetreg
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 offsetreg.
bsvarSIGNs
Sign, zero and narrative restrictions brought into the bsvars ecosystem.
estimatr
Fast design-based estimators for experiments, coasting on CRAN patches.
ggdist
The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.
ipaddress
IP address vectors for R that hit 1.0 and then went quiet.
ltertools
A column-key toolkit for stitching decades of ecological field data into one table.
See all driveR alternatives → · See all offsetreg alternatives →
Recent activity from driveR and offsetreg
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 4mo agooffsetregArgument passthrough to glm and glmnet, xgboost 3.0 minimum
- 7mo agodriveRGene-level copy number input accepted, annotation packages made optional
- 1y agooffsetregBoosted trees and weighted decision trees join the offset model family ⚡
- 2y agooffsetregInitial release with Poisson regression over offsets
- 3y agodriveRCRAN documentation error fixed
- 4y agodriveRGRCh38 genome build supported
- 4y agodriveRCancer-type-specific thresholds updated
- 5y agodriveRMCR coordinates converted to hg19 and the model retrained
- 5y agodriveRCopy number score was never being computed, model rebuilt
Frequently asked questions
What is the difference between driveR and offsetreg?
Both compete on the same themes — r-package, machine-learning — within Infra & APIs. driveR and offsetreg 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.
Is driveR better than offsetreg?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. driveR and offsetreg 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.
What are the best alternatives to driveR?
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.
What are the best alternatives to offsetreg?
Top offsetreg alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "offsetreg alternatives" section above for the current picks, or visit /alternatives/offsetreg for the full list with editorial commentary on each.