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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | ivgls_0.1.0.tar.gz |
60.3 KiB |
0.1.0 |
rolling linux/noble R-4.5 | ivgls_0.1.0.tar.gz |
60.1 KiB |
0.1.0 |
rolling source/ R- | ivgls_0.1.0.tar.gz |
19.3 KiB |
0.1.0 |
latest linux/jammy R-4.5 | ivgls_0.1.0.tar.gz |
60.3 KiB |
0.1.0 |
latest linux/noble R-4.5 | ivgls_0.1.0.tar.gz |
60.1 KiB |
0.1.0 |
latest source/ R- | ivgls_0.1.0.tar.gz |
19.3 KiB |
0.1.0 |
2026-04-23 source/ R- | ivgls_0.1.0.tar.gz |
0 B |