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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.
graphicalMCP
is currently not on CRAN but can be
installed from GitHub using the following code:
# install.packages("pak")
::pak("Gilead-BioStats/graphicalMCP") pak
vignette("graphicalMCP")
vignette("glossary")
graphicalMCP
,
vignette("shortcut-testing")
for sequentially
rejective graphical multiple comparison procedures based on Bonferroni
testsvignette("closed-testing")
for graphical multiple
comparison procedures based on the closure principlevignette("graph-examples")
for common multiple
comparison procedures illustrated using graphicalMCP
vignette("generate-closure")
for rationales to
generate the closure and the weighting strategy of a graphvignette("comparisons")
for comparisons to other R
packagesgraphicalMCP
from GitHub, we can build vignettes by
devtools::install(build_vignettes = TRUE)
, and then use
browseVignettes("graphicalMCP")
to view the full list of
vignettesgMCP
which removes the rJava
dependency - gMCPLiteBuilt 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, 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.