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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.