HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
Comparison · Infra & APIs
BayesianMCPMod vs Luminescence
A side-by-side editorial comparison of BayesianMCPMod and Luminescence — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
BayesianMCPMod vs Luminescence: at a glance
What is BayesianMCPMod?
A Bayesian dose-finding package extends from continuous endpoints to binary ones
BayesianMCPMod implements the Bayesian form of MCP-Mod for dose-finding trials, combining a multiple-comparison test for dose-response signal with model fitting for dose selection. Version 1.3.0 added functions and vignettes for the binary endpoint case, opening the package beyond the continuous endpoints it was built around, and 1.3.2 followed with Firth's penalized regression to handle separation in those binary fits. The same 1.3.0 release let assessDesign() accept custom simulated data and custom model estimates, which moves simulation control out of the package and into the user's hands.
What is Luminescence?
Luminescence is revisiting the statistical assumptions baked into its dose-response fits.
The package provides luminescence dating analysis for the geochronology community — signal import from several instrument formats, dose-response curve fitting, and a large library of analysis routines. The current release changes how fitting weights are handled by default, on the reasoning that individual relative uncertainties do not in fact scale with dose as the previous approach assumed. Several releases in this window are historical versions backfilled into the feed, so their timestamps sit within minutes of each other and do not reflect when the work shipped.
BayesianMCPMod vs Luminescence: editorial side-by-side
BayesianMCPMod
0.0INFRA · APIS
A Bayesian dose-finding package extends from continuous endpoints to binary ones
◆ Current state
BayesianMCPMod implements the Bayesian form of MCP-Mod for dose-finding trials, combining a multiple-comparison test for dose-response signal with model fitting for dose selection. Version 1.3.0 added functions and vignettes for the binary endpoint case, opening the package beyond the continuous endpoints it was built around, and 1.3.2 followed with Firth's penalized regression to handle separation in those binary fits. The same 1.3.0 release let assessDesign() accept custom simulated data and custom model estimates, which moves simulation control out of the package and into the user's hands.
◆ Where it's heading
Each release has widened the estimands and data shapes the framework accepts rather than changing its statistical core. 1.0.2 added non-monotonic beta and quadratic model shapes; 1.1.0 introduced getMED() for the minimally efficacious dose and parallel execution through the future framework; 1.2.0 switched the posterior and contrast functions from a standard deviation vector to a full covariance matrix and supported non-zero off-diagonals in the MCP step. The binary endpoint work is the same pattern applied to the outcome type, and the Firth addition shows the follow-through of a maintainer who has hit the separation problem in practice.
◆ Prediction
Expect the binary endpoint arm to keep filling in - more diagnostics and design assessment coverage matching what the continuous case already has - since 1.3.2 addressed a specific estimation failure rather than adding a new capability.
Luminescence
2.5INFRA · APIS
Luminescence is revisiting the statistical assumptions baked into its dose-response fits.
◆ Current state
The package provides luminescence dating analysis for the geochronology community — signal import from several instrument formats, dose-response curve fitting, and a large library of analysis routines. The current release changes how fitting weights are handled by default, on the reasoning that individual relative uncertainties do not in fact scale with dose as the previous approach assumed. Several releases in this window are historical versions backfilled into the feed, so their timestamps sit within minutes of each other and do not reflect when the work shipped.
◆ Where it's heading
Two kinds of change dominate. The first is deliberate breaking changes made for correctness or consistency: the weighting rework, and import functions now uniformly appending the detector to record types so all formats behave as one always did. The second is a steady stream of new analysis functions contributed by the wider research community, covering crosstalk correction, spatial autocorrelation on grain discs, and incomplete bleaching models. Deprecated functions are being removed on a clear schedule rather than left indefinitely.
◆ Prediction
Expect further consolidation of defaults that were inherited rather than chosen, since the weighting change is framed as correcting earlier reasoning rather than adding an option.
Alternatives to BayesianMCPMod and Luminescence
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 BayesianMCPMod or Luminescence.
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 BayesianMCPMod alternatives → · See all Luminescence alternatives →
Recent activity from BayesianMCPMod and Luminescence
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 26d agoLuminescenceReworks dose-response fitting weights, changing prior defaults
- 3mo agoBayesianMCPModFirth penalized regression handles separation in binary endpoints
- 4mo agoLuminescenceFixes an uninitialised array and background overcounting
- 5mo agoLuminescenceImport functions now append detector type to record names
- 5mo agoBayesianMCPModRegression fix for missing future.apply, plus credible band options
- 5mo agoBayesianMCPModBinary endpoint support opens the framework past continuous outcomes ⚡
- 7mo agoLuminescenceAdds crosstalk correction, Moran's I, and dose-response fitting
- 7mo agoLuminescenceFixes baSAR reporting, fading input checks, and plot regressions
- 7mo agoLuminescenceAdds automatic background subtraction and record removal helpers
- 11mo agoBayesianMCPModCovariance matrices replace standard deviation vectors in the MCP step
- 1y agoBayesianMCPModMinimally efficacious dose estimation and parallel execution
- 1y agoBayesianMCPModNon-monotonic beta and quadratic dose-response shapes
Frequently asked questions
What is the difference between BayesianMCPMod and Luminescence?
Both compete on the same themes — r-package — within Infra & APIs. Luminescence is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is BayesianMCPMod better than Luminescence?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Luminescence is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
What are the best alternatives to BayesianMCPMod?
Top BayesianMCPMod alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "BayesianMCPMod alternatives" section above for the current picks, or visit /alternatives/bayesianmcpmod for the full list with editorial commentary on each.
What are the best alternatives to Luminescence?
Top Luminescence alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "Luminescence alternatives" section above for the current picks, or visit /alternatives/luminescence for the full list with editorial commentary on each.