Package: rgm 1.0.1
Francisco Richter
rgm: Advanced Inference with Random Graphical Models
Implements state-of-the-art Random Graphical Models (RGMs) for multivariate data analysis across multiple environments, offering tools for exploring network interactions and structural relationships. Capabilities include joint inference across environments, integration of external covariates, and a Bayesian framework for uncertainty quantification. Applicable in various fields, including microbiome analysis. Methods based on Vinciotti, V., Wit, E., & Richter, F. (2023). "Random Graphical Model of Microbiome Interactions in Related Environments." <arxiv:2304.01956>.
Authors:
rgm_1.0.1.tar.gz
rgm_1.0.1.zip(r-4.5)rgm_1.0.1.zip(r-4.4)rgm_1.0.1.zip(r-4.3)
rgm_1.0.1.tgz(r-4.4-x86_64)rgm_1.0.1.tgz(r-4.4-arm64)rgm_1.0.1.tgz(r-4.3-x86_64)rgm_1.0.1.tgz(r-4.3-arm64)
rgm_1.0.1.tar.gz(r-4.5-noble)rgm_1.0.1.tar.gz(r-4.4-noble)
rgm_1.0.1.tgz(r-4.4-emscripten)rgm_1.0.1.tgz(r-4.3-emscripten)
rgm.pdf |rgm.html✨
rgm/json (API)
# Install 'rgm' in R: |
install.packages('rgm', repos = c('https://franciscorichter.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/franciscorichter/rgm/issues
Last updated 8 months agofrom:67c9a11af6. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 06 2024 |
R-4.5-win-x86_64 | OK | Nov 06 2024 |
R-4.5-linux-x86_64 | OK | Nov 06 2024 |
R-4.4-win-x86_64 | OK | Nov 06 2024 |
R-4.4-mac-x86_64 | OK | Nov 06 2024 |
R-4.4-mac-aarch64 | OK | Nov 06 2024 |
R-4.3-win-x86_64 | OK | Nov 06 2024 |
R-4.3-mac-x86_64 | OK | Nov 06 2024 |
R-4.3-mac-aarch64 | OK | Nov 06 2024 |
Exports:bprGmcmcrgmrotsample.datasim.rgm
Dependencies:BDgraphclicolorspacecpp11fansifarverggplot2gluegtablehugeigraphisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrpROCR6RColorBrewerRcppRcppEigenrlangscalestibbletruncnormutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Bayesian Probit Regression (BPR) | bpr |
Graph MCMC Sampler | Gmcmc |
Random Graphical Model | rgm |
Rotate Locations | rot |
Sample Data | sample.data |
Simulate Data from a Random Graphical Model | sim.rgm |