keaven/gMCPLite

Graphical MCP with gMCPLite (Legacy)

For new projects, prefer the graphicalMCP package which has a cleaner API.

API reference

• Full function docs: references/llms.txt (built from local man pages)
• Workflow patterns: references/code_patterns.md

Key functions

Graph creation

• matrix2graph() - Create graphMCP object from transition matrix
• graphMCP class - Core graph representation (hypotheses, weights, transitions)
• joinGraphs() - Combine multiple graphs
• subgraph() - Extract subgraph

Testing

• gMCP() - Graphical MCP testing procedure
• gMCP.extended() - Extended testing with parametric tests
• graphTest() - Test hypotheses on a graph

Visualization

• hGraph() - Create multiplicity graph visualization (ggplot2-based)
• placeNodes() - Compute node positions for graph layout

Test functions

• bonferroni.test() - Bonferroni test
• bonferroni.trimmed.simes.test() - Bonferroni-trimmed Simes test
• parametric.test() - Parametric test using correlation
• simes.test() - Simes test
• simes.on.subsets.test() - Simes test on subsets

Utilities

• generateWeights() - Generate weights for intersection hypotheses
• generatePvals() - Generate p-values for simulation
• simConfint() - Simultaneous confidence intervals
• rejectNode() - Reject a hypothesis and update graph
• exampleGraphs() - Pre-built example graphs
• checkCorrelation() - Validate correlation matrix

Workflow patterns

For detailed code templates, read references/code_patterns.md.

Topics covered:

• Creating multiplicity graphs with hGraph() (basic and custom)
• Customizing hGraph layout (positions, colors, legends, sizing, radianStart)
• Creating graphMCP objects with matrix2graph()
• Bonferroni, Simes, and parametric testing with gMCP()
• Extended testing with gMCP.extended() and custom test functions
• Generating intersection weights with generateWeights()
• Updating graphs after rejection with rejectNode()
• Simultaneous confidence intervals with simConfint()
• Built-in example graphs (BonferroniHolm, fixedSequence, fallback, etc.)
• Integration with gsDesign sequential p-values (sequentialPValue())
• Complex oncology trial template (6 hypotheses: OS/PFS/ORR x Subgroup/All)
• Combining and subsetting graphs (joinGraphs(), subgraph())

Important design considerations

• For new projects, prefer graphicalMCP: It has a cleaner S3 API (graph_create, graph_test_shortcut, graph_test_closure) and is actively maintained
• hGraph() remains widely used: Even with graphicalMCP for testing, hGraph() from gMCPLite is commonly used for visualization in publications and presentations
• Sequential p-values workflow: Use gsDesign::sequentialPValue() to convert nominal p-values from group sequential analyses into sequential p-values, then pass to gMCP() for multiplicity control
• upscale = TRUE: Required for parametric tests (Bretz et al. 2011) to rescale subgraph weights to sum to 1
• correlation with NA: gMCPLite supports partially specified correlation matrices (NA for unknown entries)
• Time travel for alpha: When a hypothesis is rejected at a later analysis, previously tested hypotheses can be re-tested at updated alpha levels — this controls Type I error but requires careful bound re-derivation
• gMCP() returns gMCPResult: Access @rejected (logical), @adjPValues (adjusted p-values), and @graphs (sequence of updated graphs)