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>.

Version:	0.1.0
Imports:	glmnet, MASS, igraph
Suggests:	knitr, glmgraph, rmarkdown, testthat (≥ 3.0.0), ggplot2, spelling
Published:	2026-06-24
DOI:	10.32614/CRAN.package.ivgls (may not be active yet)
Author:	Dhrubajyoti Ghosh [aut, cre], Samhita Pal [aut]
Maintainer:	Dhrubajyoti Ghosh <dghosh3 at kennesaw.edu>
BugReports:	https://github.com/djghosh1123/ivgls/issues
License:	MIT + file LICENSE
URL:	https://github.com/djghosh1123/ivgls
NeedsCompilation:	no
Additional_repositories:	https://djghosh1123.r-universe.dev
Language:	en-US
Materials:	README
CRAN checks:	ivgls results

Documentation:

Reference manual:	ivgls.html , ivgls.pdf
Vignettes:	Getting Started with ivgls (source, R code)

Downloads:

Package source:	ivgls_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: ivgls_0.1.0.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): ivgls_0.1.0.tgz, r-oldrel (arm64): ivgls_0.1.0.tgz, r-release (x86_64): ivgls_0.1.0.tgz, r-oldrel (x86_64): ivgls_0.1.0.tgz

Linking:

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