ivgls

ivgls implements network-aware instrumental variable (IV) regression with a graph-fused Lasso penalty for causal variable selection in high-dimensional, graph-structured settings.

Estimators

Function	Graph penalty	Invalid-IV robust
`iv_lasso()`	No	No
`ivgl()`	Yes	No
`ivgl_s()`	Yes	Yes

Installation

glmgraph is required but not on CRAN — install it first:

devtools::install_github("cran/glmgraph")
install.packages("ivgls")

Quick Example

library(ivgls)

set.seed(1)
A    <- make_graph(p = 20, type = "chain")
L    <- get_laplacian(A)
bobj <- generate_beta(A, s2 = 4, signal = 2)
dat  <- generate_data(n = 120, p = 20, q = 60,
                      s_alpha = 5, alpha_strength = 3,
                      beta_true = bobj$beta_true)

fit <- ivgl_s(dat$Y, dat$X, dat$Z, L)
get_mcc(bobj$active_set, which(abs(fit$beta) > 1e-4), p = 20)

Citation

Pal, S. & Ghosh, D. (2026). Network-aware IV regression for causal node discovery and estimation.