name: gMCPLite description: > Guide users through graphical MCP procedures using the gMCPLite R package (legacy). Use this skill when the user asks about: hGraph for multiplicity graph visualization, gMCP for closed testing, or legacy graphical MCP workflows. For new projects, prefer graphicalMCP.

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)