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Package {ivgls}

Type: Package
Title: Network-Aware IV Regression with Graph-Fused Lasso
Version: 0.1.0
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: glmnet, MASS, igraph
Suggests: knitr, glmgraph, rmarkdown, testthat (≥ 3.0.0), ggplot2, spelling
Additional_repositories: https://djghosh1123.r-universe.dev
VignetteBuilder: knitr
RoxygenNote: 7.3.2
Config/roxygen2/version: 8.0.0
Config/testthat/edition: 3
URL: https://github.com/djghosh1123/ivgls
BugReports: https://github.com/djghosh1123/ivgls/issues
Language: en-US
NeedsCompilation: no
Packaged: 2026-06-20 02:10:10 UTC; dghosh3
Author: Dhrubajyoti Ghosh ORCID iD [aut, cre], Samhita Pal ORCID iD [aut]
Maintainer: Dhrubajyoti Ghosh <dghosh3@kennesaw.edu>
Repository: CRAN
Date/Publication: 2026-06-24 09:30:02 UTC

Project a vector onto the column space of Z

Description

Project a vector onto the column space of Z

Usage

PZ(v, Z)

Corrupt a graph by random edge swaps

Description

Corrupt a graph by random edge swaps

Usage

corrupt_graph(A, corruption_rate = 0.3)

Arguments

A

Symmetric p x p binary adjacency matrix.

corruption_rate

Proportion of edges to remove and replace.

Value

A corrupted adjacency matrix.

Compute performance metrics for support recovery

Description

Compute performance metrics for support recovery

Usage

eval_support(true_support, estimated_support, p)

Arguments

true_support

Integer vector of true active indices.

estimated_support

Integer vector of estimated active indices.

p

Total number of predictors.

Value

Named numeric vector with MCC, TPR, FPR, and Selected.

Generate a sparse true coefficient vector on a graph

Description

Generate a sparse true coefficient vector on a graph

Usage

generate_beta(
  A,
  s2 = 5,
  signal = 3,
  pattern = c("smooth", "nonsmooth", "community"),
  smooth_noise = 0.2
)

Arguments

A

Symmetric p x p adjacency matrix.

s2

Number of active nodes.

signal

Causal effect magnitude.

pattern

One of "smooth", "nonsmooth", or "community".

smooth_noise

SD of noise added around the base signal.

Value

A list with beta_true and active_set.

Simulate data for graph-IV regression

Description

Simulate data for graph-IV regression

Usage

generate_data(
  n = 100,
  p = 70,
  q = 500,
  s1 = 0.1,
  s_alpha = 10,
  alpha_strength = 5,
  beta_true
)

Arguments

n

Sample size.

p

Number of exposures.

q

Number of instruments.

s1

Fraction of instruments relevant for each exposure.

s_alpha

Number of invalid instruments.

alpha_strength

Direct-effect magnitude of invalid instruments.

beta_true

Numeric vector of length p of true causal effects.

Value

A list with Y, X, Z, A_true, and alpha_true.

Compute the unnormalised graph Laplacian

Description

Compute the unnormalised graph Laplacian

Usage

get_laplacian(A)

Arguments

A

Symmetric p x p binary adjacency matrix.

Value

A p x p Laplacian matrix.

Matthews Correlation Coefficient for support recovery

Description

Matthews Correlation Coefficient for support recovery

Usage

get_mcc(true_support, estimated_support, p)

Arguments

true_support

Integer vector of truly active indices.

estimated_support

Integer vector of estimated active indices.

p

Total number of predictors.

Value

A scalar between -1 and 1.

IV-LASSO: Two-stage LASSO without graph structure

Description

IV-LASSO: Two-stage LASSO without graph structure

Usage

iv_lasso(Y, X, Z)

Arguments

Y

Numeric vector of length n. Outcome.

X

Numeric n x p matrix of endogenous exposures.

Z

Numeric n x q matrix of instruments.

Value

Numeric vector of length p of estimated causal effects.

IVGL: IV regression with graph-fused Lasso

Description

IVGL: IV regression with graph-fused Lasso

Usage

ivgl(Y, X, Z, L)

Arguments

Y

Numeric vector of length n. Outcome.

X

Numeric n x p matrix of endogenous exposures.

Z

Numeric n x q matrix of instruments.

L

Numeric p x p graph Laplacian (see get_laplacian).

Value

Numeric vector of length p of estimated causal effects.

IVGL-S: IV regression with graph Lasso and invalid-IV correction

Description

Extends IVGL with an alternating sisVIVE-style update to handle partially invalid instruments that violate the exclusion restriction.

Usage

ivgl_s(Y, X, Z, L, max_iter = 20, verbose = FALSE)

Arguments

Y

Numeric vector of length n. Outcome.

X

Numeric n x p matrix of endogenous exposures.

Z

Numeric n x q matrix of instruments.

L

Numeric p x p graph Laplacian.

max_iter

Maximum number of alternating iterations. Default 20.

verbose

Print CV loss at each iteration. Default FALSE.

Value

A list with beta (length p causal effects) and alpha (length q direct IV-outcome effects).

Construct a graph adjacency matrix

Description

Construct a graph adjacency matrix

Usage

make_graph(
  p = 70,
  type = c("proximity", "ring", "chain", "community", "disconnected")
)

Arguments

p

Number of nodes.

type

One of "proximity", "ring", "chain", "community", or "disconnected".

Value

A symmetric p x p binary adjacency matrix.

Run a single simulation replicate

Description

Run a single simulation replicate

Usage

run_one_replicate(
  n = 100,
  p = 70,
  q = 500,
  graph_type = "proximity",
  signal_pattern = "smooth",
  fit_graph_type = NULL,
  graph_corruption = 0,
  s2 = 5,
  signal = 3,
  s_alpha = 10,
  alpha_strength = 5,
  smooth_noise = 0.2,
  threshold = 1e-04
)

Arguments

n

Sample size.

p

Number of exposures.

q

Number of instruments.

graph_type

Graph topology passed to make_graph.

signal_pattern

One of "smooth", "nonsmooth", or "community".

fit_graph_type

Graph supplied to estimators. If NULL uses graph_type.

graph_corruption

Proportion of edges to corrupt. Default 0.

s2

Number of active nodes.

signal

Causal effect magnitude.

s_alpha

Number of invalid instruments.

alpha_strength

Invalid-IV direct-effect magnitude.

smooth_noise

Noise on the smooth signal pattern.

threshold

Coefficients below this are treated as zero.

Value

A data.frame with one row per method and columns Method, MSE, MCC, TPR, FPR, Selected.

Run a simulation study with multiple replicates

Description

Run a simulation study with multiple replicates

Usage

run_simulation(
  B = 100,
  n = 100,
  p = 70,
  q = 500,
  graph_type = "proximity",
  signal_pattern = "smooth",
  fit_graph_type = NULL,
  graph_corruption = 0,
  s2 = 5,
  signal = 3,
  s_alpha = 10,
  alpha_strength = 5,
  smooth_noise = 0.2,
  threshold = 1e-04
)

Arguments

B

Number of Monte Carlo replicates.

n

Sample size.

p

Number of exposures.

q

Number of instruments.

graph_type

Graph topology passed to make_graph.

signal_pattern

One of "smooth", "nonsmooth", or "community".

fit_graph_type

Graph supplied to estimators. If NULL uses graph_type.

graph_corruption

Proportion of edges to corrupt. Default 0.

s2

Number of active nodes.

signal

Causal effect magnitude.

s_alpha

Number of invalid instruments.

alpha_strength

Invalid-IV direct-effect magnitude.

smooth_noise

Noise on the smooth signal pattern.

threshold

Coefficients below this are treated as zero.

Value

A data.frame with 3*B rows, one per method per replicate.

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.