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OHPL vs RMVMR

A side-by-side editorial comparison of OHPL and RMVMR — release velocity, themes, recent moves, and the top alternatives to consider.

OHPL vs RMVMR: at a glance

FeatureOHPLRMVMR
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themeschemometrics, variable-selection, spectroscopy, archival-maintenancemendelian randomization, r, radial methods, genetics
Last editorial update42m ago1h ago
WebsiteVisit →Visit →

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.

Read the full OHPL trajectory →

What is RMVMR?

RMVMR is being tidied in lockstep with MVMR, the package it wraps

RMVMR provides radial multivariable Mendelian randomization — radial IVW estimation and plots layered over the MVMR package's conditional instrument-strength machinery. It has no independent release schedule: versions arrive alongside MVMR's, pin a minimum MVMR version, and fix defects in the seam between the two. Its most recent release landed the same day as new versions of MVMR and OneSampleMR.

Read the full RMVMR trajectory →

OHPL vs RMVMR: editorial side-by-side

O
OHPL
ANALYTICS
0.0

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.

R
RMVMR
ANALYTICS
0.0

RMVMR is being tidied in lockstep with MVMR, the package it wraps

◆ Current state

RMVMR provides radial multivariable Mendelian randomization — radial IVW estimation and plots layered over the MVMR package's conditional instrument-strength machinery. It has no independent release schedule: versions arrive alongside MVMR's, pin a minimum MVMR version, and fix defects in the seam between the two. Its most recent release landed the same day as new versions of MVMR and OneSampleMR.

◆ Where it's heading

The work is code hygiene with results held fixed. ivw_rmvmr() now fits the radial IVW model explicitly rather than inheriting variables left over from the orientation loop, an undocumented data element carrying unused intermediate frames is gone, and plot_rmvmr() stops recomputing univariate radial analyses it already has — roughly halving the RadialMR calls with identical output. Each note is explicit that coefficients, standard errors and degrees of freedom are unchanged, which is a deliberate contrast with MVMR's own 2026 releases, where several fixes did change reported values.

◆ Prediction

Because the package pins MVMR versions rather than vendoring behaviour, the next release most likely follows MVMR's next correctness fix; nothing in the notes points to independent feature work.

Alternatives to OHPL and RMVMR

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 RMVMR.

See all OHPL alternatives → · See all RMVMR alternatives →

Recent activity from OHPL and RMVMR

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agoRMVMRRMVMR fixes a gencov error and halves redundant radial calls
  2. 3mo agoRMVMRRMVMR 0.4.4
  3. 4mo agoOHPLRd documentation HTML validation fixed
  4. 4mo agoRMVMRRMVMR 0.4.3
  5. 5mo agoRMVMRRMVMR now requires MVMR 0.4.3 or later
  6. 1y agoRMVMRRMVMR 0.4.1
  7. 2y agoOHPLDocumentation modernized to current R conventions
  8. 7y agoOHPLCode restyled and repository links updated
  9. 9y agoOHPLCitation information and documentation site updated
  10. 9y agoOHPLPrediction and evaluation functions complete the package

Frequently asked questions

What is the difference between OHPL and RMVMR?

They serve adjacent needs but don't currently overlap on shipped themes. OHPL and RMVMR 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 RMVMR?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. OHPL and RMVMR 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 RMVMR?

Top RMVMR alternatives in Analytics are ranked by recent ship velocity. Browse the "RMVMR alternatives" section above for the current picks, or visit /alternatives/rmvmr for the full list with editorial commentary on each.