HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
Comparison · Infra & APIs
BayesianMCPMod vs finnts
A side-by-side editorial comparison of BayesianMCPMod and finnts — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
BayesianMCPMod vs finnts: 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 finnts?
Microsoft's automated forecasting framework, still mostly a one-maintainer effort.
finnts automates time-series forecasting end to end — feature engineering, model selection, hierarchical reconciliation — on a tidymodels backbone. Recent releases have concentrated on global models (one model fitted across many series) and on hierarchical reconciliation, which has needed repeated correction at weekly granularity. Release notes are auto-generated pull-request lists, so the detail lives in the PRs rather than in the changelog.
BayesianMCPMod vs finnts: 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.
finnts
0.0INFRA · APIS
Microsoft's automated forecasting framework, still mostly a one-maintainer effort.
◆ Current state
finnts automates time-series forecasting end to end — feature engineering, model selection, hierarchical reconciliation — on a tidymodels backbone. Recent releases have concentrated on global models (one model fitted across many series) and on hierarchical reconciliation, which has needed repeated correction at weekly granularity. Release notes are auto-generated pull-request lists, so the detail lives in the PRs rather than in the changelog.
◆ Where it's heading
Cadence is roughly annual and the commit history is almost entirely one maintainer, with occasional outside contributions. The direction across the last four releases is consolidation of the forecasting internals — multi-horizon models, feature selection, reconciliation fixes — rather than new surface for users. The changelogs themselves are unedited PR dumps, which makes the arc harder to read than the work probably warrants.
◆ Prediction
Hierarchical reconciliation has produced a bug fix in three of the last four releases, so the next one likely touches it again; nothing in the entries points to a specific new capability.
Alternatives to BayesianMCPMod and finnts
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 finnts.
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estimatr
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See all BayesianMCPMod alternatives → · See all finnts alternatives →
Recent activity from BayesianMCPMod and finnts
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agoBayesianMCPModFirth penalized regression handles separation in binary endpoints
- 5mo agoBayesianMCPModRegression fix for missing future.apply, plus credible band options
- 5mo agoBayesianMCPModBinary endpoint support opens the framework past continuous outcomes ⚡
- 11mo agofinntsGlobal model updates and a weekly reconciliation fix
- 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
- 1y agofinntsMulti-horizon models, hierarchy drivers, Synapse 3.4 migration
- 2y agofinntsARIMAX support and feature selection added
- 3y agofinntsdplyr 1.1.0 compatibility and a reconciliation fix
Frequently asked questions
What is the difference between BayesianMCPMod and finnts?
Both compete on the same themes — r-package — within Infra & APIs. BayesianMCPMod and finnts 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 BayesianMCPMod better than finnts?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. BayesianMCPMod and finnts 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 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 finnts?
Top finnts alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "finnts alternatives" section above for the current picks, or visit /alternatives/finnts for the full list with editorial commentary on each.