BayesianMCPMod vs Apache CloudStack

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.

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.

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.