HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
Comparison · Infra & APIs
BayesianMCPMod vs simDAG
A side-by-side editorial comparison of BayesianMCPMod and simDAG — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
BayesianMCPMod vs simDAG: 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 simDAG?
simDAG grew a second simulation engine, then spent two releases surviving upstream breakage.
simDAG generates data from directed acyclic graphs, with a library of node types covering Gaussian, binomial, Poisson, negative binomial, zero-inflated, ordered regression, Cox, and Aalen models. The 1.0.0 milestone opened node_cox() to arbitrary baseline hazard functions, which lets continuous time-dependent hazards drive discrete-event simulations. The two most recent releases exist only to keep the package on CRAN through breakage in lme4 and simr.
BayesianMCPMod vs simDAG: 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.
simDAG
2.5INFRA · APIS
simDAG grew a second simulation engine, then spent two releases surviving upstream breakage.
◆ Current state
simDAG generates data from directed acyclic graphs, with a library of node types covering Gaussian, binomial, Poisson, negative binomial, zero-inflated, ordered regression, Cox, and Aalen models. The 1.0.0 milestone opened node_cox() to arbitrary baseline hazard functions, which lets continuous time-dependent hazards drive discrete-event simulations. The two most recent releases exist only to keep the package on CRAN through breakage in lme4 and simr.
◆ Where it's heading
The package has been widening what a simulation can represent rather than deepening any one node. Networks arrived in 0.4.0 so individuals could depend on each other, discrete-event simulation in continuous time arrived in 0.5.0 as an alternative to the discrete-time engine, and 1.0.0 connected the two by letting continuous hazards feed the event-driven path. Alongside that, node types keep accumulating for outcome families the framework could not previously generate.
◆ Prediction
Expect the node library to keep expanding into outcome types the discrete-event engine can now support, though the recent releases suggest upstream dependency churn will keep consuming release slots.
Alternatives to BayesianMCPMod and simDAG
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 simDAG.
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 simDAG alternatives →
Recent activity from BayesianMCPMod and simDAG
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 18d agosimDAGCRAN-retention patch for upstream lme4 breakage
- 3mo agosimDAGArbitrary baseline hazards connect node_cox() to discrete-event sims
- 3mo agoBayesianMCPModFirth penalized regression handles separation in binary endpoints
- 4mo agosimDAGTest-only fix for an upstream simr update
- 5mo agosimDAGAdds node_polr() for ordinal outcomes and rsurv node support
- 5mo agoBayesianMCPModRegression fix for missing future.apply, plus credible band options
- 5mo agoBayesianMCPModBinary endpoint support opens the framework past continuous outcomes ⚡
- 7mo agosimDAGAdds continuous-time discrete-event simulation ⚡
- 10mo agosimDAGAdds link functions to node types and fixes a broken seed default
- 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 simDAG?
Both compete on the same themes — r-package — within Infra & APIs. simDAG 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 simDAG?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. simDAG 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 simDAG?
Top simDAG alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "simDAG alternatives" section above for the current picks, or visit /alternatives/simdag for the full list with editorial commentary on each.