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README

Graphical approaches for multiple comparison procedures (MCPs) are a general framework to control the family-wise error rate strongly at a pre-specified significance level \(0<\alpha<1\). This approach includes many commonly used MCPs as special cases and is transparent in visualizing MCPs for better communications. graphicalMCP is designed to design and analyze graphical MCPs in a flexible, informative and efficient way.

You can install the current release version from CRAN with:

install.packages("graphicalMCP")

You can install the current development version from GitHub with:

# install.packages("pak")
pak::pak("openpharma/graphicalMCP")

• For basic usage instructions, see vignette("graphicalMCP")
• To become familiar with graphical MCP terminologies, see vignette("glossary")
• To learn examples of how to use graphicalMCP,
  • see vignette("shortcut-testing") for sequentially rejective graphical multiple comparison procedures based on Bonferroni tests
  • see vignette("closed-testing") for graphical multiple comparison procedures based on the closure principle using Bonferroni, Hochberg, parametric and Simes tests
  • see vignette("graph-examples") for common multiple comparison procedures illustrated using graphicalMCP
  • see vignette("internal-validation") for internal validation via power simulations for methods used in graphicalMCP
  • see vignette("generate-closure") for rationales to generate the closure and the weighting strategy of a graph
  • see vignette("comparisons") for comparisons to other R packages
• To view vignettes in R after properly installing graphicalMCP, we can build vignettes by devtools::install(build_vignettes = TRUE), and then use browseVignettes("graphicalMCP") to view the full list of vignettes

• Graphical MCPs - gMCP
• Lighter version of gMCP which removes the rJava dependency - gMCPLite
• Graphical MCPs with Simes tests - lrstat

Built upon these packages, we hope to implement graphical MCPs in a more general framework, with fewer dependencies and simpler S3 classes, and without losing computational efficiency.

Along with the authors and contributors, thanks to the following people for their suggestions and inspirations on the package:

Frank Bretz, Willi Maurer, Ekkehard Glimm, Nan Chen, Jeremy Wildfire, Spencer Childress, Colleen McLaughlin, Matt Roumaya, Chelsea Dickens, Nan Xiao, Keaven Anderson, and Ron Yu

We owe a debt of gratitude to the authors of gMCP for their pioneering work, without which this package would not be nearly as extensive as it is.

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