HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
Comparison · Infra & APIs
BayesianMCPMod vs OptimalBinningWoE
A side-by-side editorial comparison of BayesianMCPMod and OptimalBinningWoE — release velocity, themes, recent moves, and the top alternatives to consider.
BayesianMCPMod vs OptimalBinningWoE: 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 OptimalBinningWoE?
OptimalBinningWoE spent two releases auditing a C++ engine that was crashing R sessions.
The package wraps 37 binning algorithms in C++, and the last two releases have been dedicated audits of that engine rather than new functionality. The 1.11.0 runtime audit found a segmentation fault in categorical binning that killed the R session for any predictor with no more levels than max_bins — with the default of five, that covers sex, marital status, region, and education. Earlier releases were CRAN compliance patches.
BayesianMCPMod vs OptimalBinningWoE: 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.
OptimalBinningWoE
2.5INFRA · APIS
OptimalBinningWoE spent two releases auditing a C++ engine that was crashing R sessions.
◆ Current state
The package wraps 37 binning algorithms in C++, and the last two releases have been dedicated audits of that engine rather than new functionality. The 1.11.0 runtime audit found a segmentation fault in categorical binning that killed the R session for any predictor with no more levels than max_bins — with the default of five, that covers sex, marital status, region, and education. Earlier releases were CRAN compliance patches.
◆ Where it's heading
The engineering practice is visibly maturing: a static audit in 1.10.0, then a runtime audit in 1.11.0 driven by address and undefined-behaviour sanitizers, a degenerate-input stress harness, and a golden-output regression suite of roughly 3,200 comparisons, with every fix pinned by a test that fails on the prior version. No public API has changed across either release. The package is buying back trust in results that were silently wrong or unreproducible.
◆ Prediction
With the audit programme apparently complete across both static and runtime passes, the next release is more likely to resume feature work on the binning algorithms than to continue hardening.
Alternatives to BayesianMCPMod and OptimalBinningWoE
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 OptimalBinningWoE.
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 OptimalBinningWoE alternatives →
Recent activity from BayesianMCPMod and OptimalBinningWoE
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 5d agoOptimalBinningWoEv1.11.0 — Runtime audit: crash, hang, data-loss and reproducibility fixes
- 3mo agoOptimalBinningWoEv1.10.0 — C++ Engine Audit & Hardening
- 3mo agoBayesianMCPModFirth penalized regression handles separation in binary endpoints
- 5mo agoOptimalBinningWoECRAN fix: proper C++ stack unwinding in error paths
- 5mo agoBayesianMCPModRegression fix for missing future.apply, plus credible band options
- 5mo agoBayesianMCPModBinary endpoint support opens the framework past continuous outcomes ⚡
- 6mo agoOptimalBinningWoEFixes LTO link errors from One Definition Rule violations
- 6mo agoOptimalBinningWoERemoves sketch binning cache while chasing UBSAN errors
- 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 OptimalBinningWoE?
They serve adjacent needs but don't currently overlap on shipped themes. OptimalBinningWoE 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 OptimalBinningWoE?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. OptimalBinningWoE 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 OptimalBinningWoE?
Top OptimalBinningWoE alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "OptimalBinningWoE alternatives" section above for the current picks, or visit /alternatives/optimalbinningwoe for the full list with editorial commentary on each.