Package: graphicalMCP 0.3.0.9000

Dong Xi

graphicalMCP: Graphical Multiple Comparison Procedures

Multiple comparison procedures (MCPs) control the familywise error rate in clinical trials. Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs.

Authors:Dong Xi [aut, cre], Ethan Brockmann [aut], Gilead Sciences, Inc. [cph, fnd]

graphicalMCP_0.3.0.9000.tar.gz
graphicalMCP_0.3.0.9000.zip(r-4.7-any)graphicalMCP_0.3.0.9000.zip(r-4.6-any)graphicalMCP_0.3.0.9000.zip(r-4.5-any)
graphicalMCP_0.3.0.9000.tgz(r-4.6-any)graphicalMCP_0.3.0.9000.tgz(r-4.5-any)
graphicalMCP_0.3.0.9000.tar.gz(r-4.7-any)graphicalMCP_0.3.0.9000.tar.gz(r-4.6-any)
graphicalMCP_0.3.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
graphicalMCP/json (API)

# Install 'graphicalMCP' in R:
install.packages('graphicalMCP', repos = c('https://openpharma.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/openpharma/graphicalmcp/issues

Pkgdown/docs site:https://openpharma.github.io

On CRAN:

Conda:

8.01 score 23 stars 46 scripts 529 downloads 48 exports 2 dependencies

Last updated from:74f2f31e3e. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE195
source / vignettesOK425
linux-releaseNOTE186
macos-releaseNOTE143
macos-oldrelNOTE177
windows-develNOTE210
windows-releaseNOTE227
windows-oldrelNOTE229
wasm-releaseOK121

Exports:adjust_p_bonferroniadjust_p_hochbergadjust_p_parametricadjust_p_simesadjust_weights_hochbergadjust_weights_parametricadjust_weights_simesas_graphMCPas_igraphas_initial_graphbonferronibonferroni_holmbonferroni_holm_weightedbonferroni_weighteddunnett_closure_weighteddunnett_single_stepdunnett_single_step_weightedfallbackfallback_improved_1fallback_improved_2fixed_sequencegraph_calculate_powergraph_creategraph_generate_weightsgraph_rejection_orderingsgraph_test_closuregraph_test_shortcutgraph_test_shortcut_gsdgraph_updategs_boundariesgs_corrhochberghommelhuque_etalrandom_graphrepeated_psequential_psidaksimple_successive_1simple_successive_2spending_hsdspending_linearspending_ofspending_pocockspending_with_timespending_wtthree_doses_two_primary_two_secondarytwo_doses_two_primary_two_secondary

Dependencies:matrixStatsmvtnorm

Group Sequential Design with Graphical Approaches
Introduction | Alpha Spending Functions | From Spending to Boundaries | Repeated and Sequential P-values | Case Study using repeated p-values and look_back = FALSE | Hypotheses and Graph | Observed P-values | Running the Procedure (look_back = FALSE) | Repeated p-values | Nominal boundaries | Comparison with Paper Values | Boundary table | Case Study using sequential p-values and look_back = TRUE | Trial Setup | Graph and Alpha Allocation | P-values and Information Fractions | Sequential P-values (look_back = TRUE) | Per-hypothesis look_back | When look_back makes a difference | Customizing Spending Functions: Spending Time | Monitoring: Adjusting for Changed Final Information | Additional Examples | Different Spending Functions per Hypothesis | Using Spending Functions from Other Packages | User-Defined Spending Functions | Summary | References
Rendered fromgroup-sequential-testing.Rmdusingknitr::rmarkdown

Last update: 2026-08-26
Started: 2026-08-21

Common multiple comparison procedures illustrated using graphicalMCP
Introduction | Bonferroni-based procedures | Bonferroni test | Weighted Bonferroni test | Holm Procedure | Weighted Holm Procedure | Fixed sequence procedure | Fallback procedure | Serial gatekeeping procedure | Parallel gatekeeping procedure | Successive procedure | Hochberg-based procedures | Hochberg procedure | Simes-based procedures | Hommel procedure | Parametric procedures | Šidák test | Dunnett test | Weighted Dunnett test | Dunnett procedure | Reference
Rendered fromgraph-examples.Rmdusingknitr::rmarkdown

Last update: 2026-08-21
Started: 2024-01-23

Get started
Introduction | Basic usage | Initial graph | Update graph | Perform graphical MCPs | Power simulations | References
Rendered fromgraphicalMCP.Rmdusingknitr::rmarkdown

Last update: 2026-08-21
Started: 2024-01-23

Sequentially rejective graphical multiple comparison procedures based on Bonferroni tests
Motivating example | Create the graph | Perform the graphical multiple comparison procedure | Adjusted p-values and rejections | Obtain final and intermediate graphs after rejections | Obtain possible orders of rejections | Obtain adjusted significance levels | Power simulation | Input: Marginal power for primary hypotheses | Input: Marginal power for secondary hypotheses | Input: Correlation structure to simulate test statistics | User-defined success criteria | Output: Simulate power | Reference
Rendered fromshortcut-testing.Rmdusingknitr::rmarkdown

Last update: 2026-08-21
Started: 2024-01-23

Validation of Group Sequential Functions Against gsDesign and rpact
Introduction | 1. Alpha Spending Functions | Comparison with gsDesign | Comparison with rpact | Detailed Example | 2. Group Sequential Boundaries | 3. Correlation Matrix | 4. Repeated P-values | 5. Sequential P-values | Relationship Between Sequential and Repeated P-values | 6. Wang-Tsiatis Boundaries | Verification of boundary shape | Summary | References
Rendered fromgsd-validation.Rmdusingknitr::rmarkdown

Last update: 2026-08-21
Started: 2026-08-21

Power simulations using multiple approaches for internal validation
Introduction | Power simulations | Bonferroni tests | Hochberg tests | Simes tests | Parametric tests | Mixed tests of Bonferroni, Hochberg and Simes | Mixed tests of parametric and one of Bonferroni, Hochberg and Simes | Conclusions
Rendered frominternal-validation.Rmdusingknitr::rmarkdown

Last update: 2025-05-06
Started: 2025-05-06

Graphical multiple comparison procedures based on the closure principle
Motivating example | Create a graph | Perform the graphical multiple comparison procedure based on the closure principle | Bonferroni tests | Obtain the closure | Obtain adjusted significance levels | Mixed procedures for graphical approaches | Parametric tests for primary hypotheses | Parametric tests for primary hypotheses and Simes tests for secondary hypotheses | Power calculation | Input: Marginal power for primary hypotheses | Input: Marginal power for secondary hypotheses | Input: Correlation structure to simulate test statistics | User-defined success criteria | Output: Calculate power | Reference
Rendered fromclosed-testing.Rmdusingknitr::rmarkdown

Last update: 2024-06-04
Started: 2024-01-23

Glossary
Rendered fromglossary.Rmdusingknitr::rmarkdown

Last update: 2024-02-12
Started: 2024-01-23

Readme and manuals

Help Manual

Help pageTopics
Calculate adjusted p-valuesadjust_p_bonferroni adjust_p_hochberg adjust_p_parametric adjust_p_simes
Calculate adjusted hypothesis weightsadjust_weights_hochberg adjust_weights_parametric adjust_weights_simes
Convert between graphicalMCP, gMCP, and igraph graph classesas_graphMCP as_graphMCP.initial_graph as_igraph as_igraph.initial_graph as_initial_graph as_initial_graph.graphMCP as_initial_graph.igraph
Example graphs of commonly used multiple comparison proceduresbonferroni bonferroni_holm bonferroni_holm_weighted bonferroni_weighted dunnett_closure_weighted dunnett_single_step dunnett_single_step_weighted fallback fallback_improved_1 fallback_improved_2 fixed_sequence hochberg hommel huque_etal random_graph sidak simple_successive_1 simple_successive_2 three_doses_two_primary_two_secondary two_doses_two_primary_two_secondary
Calculate power values for a graphical multiple comparison proceduregraph_calculate_power
Create the initial graph for a multiple comparison proceduregraph_create
Generate the weighting strategy based on a graphical multiple comparison proceduregraph_generate_weights
Find alternate rejection orderings (sequences) for shortcut testsgraph_rejection_orderings
Perform closed graphical multiple comparison proceduresgraph_test_closure
Perform shortcut (sequentially rejective) graphical multiple comparison proceduresgraph_test_shortcut
Perform shortcut graphical multiple comparison procedures with group sequential designsgraph_test_shortcut_gsd
Obtain an updated graph by updating an initial graphical after deleting hypothesesgraph_update
Compute group sequential boundaries from an alpha spending functiongs_boundaries
Compute the correlation matrix for group sequential test statisticsgs_corr
S3 plot method for class 'initial_graph'plot.initial_graph
S3 plot method for the class 'updated_graph'plot.updated_graph
S3 print method for the class 'graph_report'print.graph_report
S3 print method for the class 'gsd_graph_report'print.gsd_graph_report
S3 print method for the class 'initial_graph'print.initial_graph
S3 print method for the class 'power_report'print.power_report
S3 print method for the class 'updated_graph'print.updated_graph
Calculate the repeated p-value for a single hypothesis at a given analysisrepeated_p
Calculate the sequential p-value for a single hypothesissequential_p
Alpha spending functions for group sequential designsspending_hsd spending_linear spending_of spending_pocock
Create a spending function with a custom spending timespending_with_time
Wang-Tsiatis spending functionspending_wt