HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
Comparison · Infra & APIs
BayesianMCPMod vs estimatr
A side-by-side editorial comparison of BayesianMCPMod and estimatr — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
BayesianMCPMod vs estimatr: 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 estimatr?
Fast design-based estimators for experiments, coasting on CRAN patches.
estimatr provides the design-based regression estimators the DeclareDesign ecosystem is built on — robust and cluster-robust standard errors, blocked and clustered randomization inference — implemented for speed rather than generality. The last three releases carry no substantive notes: each is a merge commit for a CRAN patch, one of them accompanied by a typo fix.
BayesianMCPMod vs estimatr: 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.
estimatr
0.0INFRA · APIS
Fast design-based estimators for experiments, coasting on CRAN patches.
◆ Current state
estimatr provides the design-based regression estimators the DeclareDesign ecosystem is built on — robust and cluster-robust standard errors, blocked and clustered randomization inference — implemented for speed rather than generality. The last three releases carry no substantive notes: each is a merge commit for a CRAN patch, one of them accompanied by a typo fix.
◆ Where it's heading
Direction cannot be read from this feed. The release notes are unedited merge-commit messages, so the only signal is cadence — roughly annual, each release framed as a CRAN patch rather than as feature work. That pattern is consistent with a package whose estimators are considered finished and which now moves only when CRAN policy requires it.
◆ Prediction
On the evidence here the next release is another CRAN compliance patch, but the notes are too thin to support a confident read of what the maintainers are actually working on.
Alternatives to BayesianMCPMod and estimatr
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 estimatr.
bsvarSIGNs
Sign, zero and narrative restrictions brought into the bsvars ecosystem.
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.
finnts
Microsoft's automated forecasting framework, still mostly a one-maintainer effort.
See all BayesianMCPMod alternatives → · See all estimatr alternatives →
Recent activity from BayesianMCPMod and estimatr
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 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 agoestimatrCRAN version 1.0.4
- 2y agoestimatrCRAN version 1.0.2
- 3y agoestimatrCRAN version 1.0.0
Frequently asked questions
What is the difference between BayesianMCPMod and estimatr?
Both compete on the same themes — r-package — within Infra & APIs. BayesianMCPMod and estimatr 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 estimatr?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. BayesianMCPMod and estimatr 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 estimatr?
Top estimatr alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "estimatr alternatives" section above for the current picks, or visit /alternatives/estimatr for the full list with editorial commentary on each.