n2kanalysis
n2kanalysis has spent eight years wiring INLA models to an S3 bucket.
Comparison · Analytics
OHPL vs treeshap
A side-by-side editorial comparison of OHPL and treeshap — release velocity, themes, recent moves, and the top alternatives to consider.
OHPL vs treeshap: at a glance
What is OHPL?
A 2017 chemometrics method frozen in place, visited only when CRAN changes its documentation rules.
OHPL implements ordered homogeneity pursuit lasso, a variable selection method for high-dimensional spectroscopic data that groups correlated predictors before applying a lasso. The functional package was complete by 1.2 in 2017, when prediction, performance evaluation and simulated data generation functions were added. Every release since has touched documentation and packaging only.
What is treeshap?
treeshap keeps widening its tree-model coverage while the SHAP math stays put.
treeshap computes exact SHAP values for tree ensembles in R, reaching each modelling framework through a per-framework unify() adapter. Since returning to CRAN in 2023 it has added GPBoost, ranger survival forests and multi-output models to that adapter layer. Four releases in three years, each dominated by adapter work contributed by users of one specific framework.
OHPL vs treeshap: editorial side-by-side
OHPL
0.0ANALYTICS
A 2017 chemometrics method frozen in place, visited only when CRAN changes its documentation rules.
◆ Current state
OHPL implements ordered homogeneity pursuit lasso, a variable selection method for high-dimensional spectroscopic data that groups correlated predictors before applying a lasso. The functional package was complete by 1.2 in 2017, when prediction, performance evaluation and simulated data generation functions were added. Every release since has touched documentation and packaging only.
◆ Where it's heading
This is a published-method package in the archival phase: the algorithm is fixed, the paper is cited, and the maintainer keeps it installable. The releases read as a timeline of R packaging conventions rather than of the method — tidyverse code style in 2019, roxygen2 Markdown and bibentry() in 2024, Rd HTML validation in 2026. Gaps of two to five years between releases are normal here.
◆ Prediction
Expect the next release whenever CRAN introduces another documentation or packaging check; there is no indication the method itself will be extended.
treeshap
0.0ANALYTICS
treeshap keeps widening its tree-model coverage while the SHAP math stays put.
◆ Current state
treeshap computes exact SHAP values for tree ensembles in R, reaching each modelling framework through a per-framework unify() adapter. Since returning to CRAN in 2023 it has added GPBoost, ranger survival forests and multi-output models to that adapter layer. Four releases in three years, each dominated by adapter work contributed by users of one specific framework.
◆ Where it's heading
The direction is breadth of model support rather than new explanation methods: every release since the first CRAN submission adds or repairs a unify() backend. Maintenance is community-driven, with named contributors fixing the framework they personally use. Nothing in these entries points at work on the SHAP algorithms themselves.
◆ Prediction
Expect the next release to add or repair another unify() adapter as a contributor brings their own framework, rather than to change how explanations are computed.
Alternatives to OHPL and treeshap
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 OHPL or treeshap.
n1qn1c
A Fortran-descended optimizer got thread-safe, then found two flags that never worked.
ggstatsplot
ggstatsplot reached 1.0 by adding tests, having outsourced its statistics years ago.
collapse
collapse got a JSS paper and a 7x fmean speedup in the same release.
gtsummary
gtsummary is quietly rebuilding itself around analysis results data, one table verb at a time.
broadcast
broadcast is filling in NumPy-style array broadcasting for R, operator by operator.
See all OHPL alternatives → · See all treeshap alternatives →
Recent activity from OHPL and treeshap
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agotreeshapGPBoost support lands; xgboost adapter repaired
- 4mo agoOHPLRd documentation HTML validation fixed
- 2y agoOHPLDocumentation modernized to current R conventions
- 2y agotreeshapFixes broken lightgbm.unify examples
- 2y agotreeshapMulti-output model explanations added
- 2y agotreeshapFirst CRAN release consolidates the unify() adapters ⚡
- 7y agoOHPLCode restyled and repository links updated
- 9y agoOHPLCitation information and documentation site updated
- 9y agoOHPLPrediction and evaluation functions complete the package
Frequently asked questions
What is the difference between OHPL and treeshap?
They serve adjacent needs but don't currently overlap on shipped themes. OHPL and treeshap 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 OHPL better than treeshap?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. OHPL and treeshap 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 Analytics products to evaluate alongside.
What are the best alternatives to OHPL?
Top OHPL alternatives in Analytics are ranked by recent ship velocity. Browse the "OHPL alternatives" section above for the current picks, or visit /alternatives/ohpl for the full list with editorial commentary on each.
What are the best alternatives to treeshap?
Top treeshap alternatives in Analytics are ranked by recent ship velocity. Browse the "treeshap alternatives" section above for the current picks, or visit /alternatives/treeshap for the full list with editorial commentary on each.