Overview | Installation | Graph construction | Generating a causal coefficient vector | Simulating data | Fitting the estimators | IV-LASSO | IVGL and IVGL-S | Performance evaluation | Graph misspecification | Simulation study | Application context
Package: ivgls 0.1.0
Dhrubajyoti Ghosh
ivgls: Network-Aware IV Regression with Graph-Fused Lasso
Implements network-aware instrumental variable regression for causal node discovery in high-dimensional settings with graph-structured exposures. Provides IVGL and IVGL-S estimators combining graph-Laplacian penalization with IV-based identification, including correction for invalid instruments via a sisVIVE-style update. Methods are described in Pal and Ghosh (2026) <doi:10.48550/arXiv.2604.24969>. The 'glmgraph' package, required for the main estimators, is available at the additional repository <https://djghosh1123.r-universe.dev>.
Authors:
ivgls_0.1.0.tar.gz
ivgls_0.1.0.tar.gz(r-4.7-any)ivgls_0.1.0.tar.gz(r-4.6-any)
ivgls_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ivgls/json (API)
| # Install 'ivgls' in R: |
| install.packages('ivgls', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/djghosh1123/ivgls/issues
Last updated from:93ba35304f. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 148 | ||
| source / vignettes | OK | 247 | ||
| linux-release-x86_64 | OK | 141 | ||
| wasm-release | OK | 185 |
Exports:corrupt_grapheval_supportgenerate_betagenerate_dataget_laplacianget_mcciv_lassoivglivgl_smake_graphrun_one_replicaterun_simulation
Dependencies:clicodetoolscpp11foreachglmnetglueigraphiteratorslatticelifecyclemagrittrMASSMatrixpkgconfigRcppRcppEigenrlangshapesurvivalvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Corrupt a graph by random edge swaps | corrupt_graph |
| Compute performance metrics for support recovery | eval_support |
| Generate a sparse true coefficient vector on a graph | generate_beta |
| Simulate data for graph-IV regression | generate_data |
| Compute the unnormalised graph Laplacian | get_laplacian |
| Matthews Correlation Coefficient for support recovery | get_mcc |
| IV-LASSO: Two-stage LASSO without graph structure | iv_lasso |
| IVGL: IV regression with graph-fused Lasso | ivgl |
| IVGL-S: IV regression with graph Lasso and invalid-IV correction | ivgl_s |
| Construct a graph adjacency matrix | make_graph |
| Run a single simulation replicate | run_one_replicate |
| Run a simulation study with multiple replicates | run_simulation |
