Package: pdglasso 2.0.1
Anna Vesely
pdglasso: Graphical Lasso for Coloured Gaussian Graphical Models for Paired Data
Implements methods for coloured Gaussian graphical models with equality "R"estrictions on "CON"centration values (RCON models; Højsgaard and Lauritzen (2008) <doi:10.1111/j.1467-9868.2008.00666.x>) for paired data (pdRCON models). Provides an Alternating Direction Method of Multipliers (ADMM) algorithm for solving the penalized likelihood problem introduced by Ranciati and Roverato (2024) <doi:10.1007/s11222-024-10513-6>. Provides functions for computing maximum likelihood estimates and generating simulated pdRCON models and datasets.
Authors:
pdglasso_2.0.1.tar.gz
pdglasso_2.0.1.zip(r-4.7-x86_64)pdglasso_2.0.1.zip(r-4.7-arm64)pdglasso_2.0.1.zip(r-4.6-x86_64)pdglasso_2.0.1.zip(r-4.6-arm64)
pdglasso_2.0.1.tar.gz(r-4.7-arm64)pdglasso_2.0.1.tar.gz(r-4.7-x86_64)pdglasso_2.0.1.tar.gz(r-4.6-arm64)pdglasso_2.0.1.tar.gz(r-4.6-x86_64)
pdglasso_2.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
pdglasso/json (API)
| # Install 'pdglasso' in R: |
| install.packages('pdglasso', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- bc_data - Breast Cancer dataset
- fMRI_parietal - FMRI dataset
- toy_data - Toy dataset generated by a call to the 'pdRCON.simulate' function
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:906fb39402. Checks:10 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 94 | ||
| linux-devel-x86_64 | OK | 111 | ||
| source / vignettes | OK | 179 | ||
| linux-release-arm64 | OK | 91 | ||
| linux-release-x86_64 | OK | 102 | ||
| windows-devel-arm64 | OK | 207 | ||
| windows-devel-x86_64 | OK | 183 | ||
| windows-release-arm64 | OK | 203 | ||
| windows-release-x86_64 | OK | 114 | ||
| wasm-release | OK | 89 |
Exports:admm.pdglassocompute.eBICGGM.simulatelams.maxpdColG.getpdRCON.mlepdRCON.selectpdRCON.simulate
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| pdglasso: Graphical Lasso for Coloured Gaussian Graphical Models for Paired Data | pdglasso-package pdglasso |
| ADMM graphical lasso algorithm for pdRCON models | admm.pdglasso |
| Breast Cancer dataset | bc_data |
| Extended Bayesian Information Criterion (eBIC) for pdRCON models | compute.eBIC |
| fMRI dataset | fMRI_parietal |
| Random simulation of Gaussian graphical models (GGMs) | GGM.simulate |
| Maximum theoretical values of lambda1 and lambda2 | lams.max |
| pdColG matrix from the output of a call to 'admm.pdglasso' | pdColG.get |
| Maximum likelihood estimate of a pdRCON model | pdRCON.mle |
| Selection and estimate of a pdRCON model according to eBIC | pdRCON.select |
| Random simulation of pdRCON models | pdRCON.simulate |
| Diagnostic plot for the output of the ADMM | plot.ADMMoutput |
| Visual representation of a coloured graph for paired data | plot.pdColG |
| Structural properties of a coloured graph for paired data | summary.pdColG |
| Toy dataset generated by a call to the 'pdRCON.simulate' function | toy_data |