Package: iglm 1.2.4

iglm: Regression under Interference in Connected Populations

An implementation of generalized linear models (GLMs) for studying relationships among attributes in connected populations, where responses of connected units can be dependent, as introduced by Fritz et al. (2025) <doi:10.1080/01621459.2025.2565851>. 'igml' extends GLMs for independent responses to dependent responses and can be used for studying spillover in connected populations and other network-mediated phenomena.

Authors:Cornelius Fritz [aut, cre], Michael Schweinberger [aut]

iglm_1.2.4.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
iglm/json (API)

# Install 'iglm' in R:
install.packages('iglm', repos = c('https://corneliusfritz.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/corneliusfritz/iglm/issues

Pkgdown/docs site:https://corneliusfritz.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

openblascppopenmp

5.23 score 1 stars 10 scripts 505 downloads 12 exports 23 dependencies

Last updated from:9af2c89e11. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK202
linux-devel-x86_64OK204
source / vignettesOK340
linux-release-arm64OK208
linux-release-x86_64OK208
macos-release-arm64OK131
macos-release-x86_64OK330
macos-oldrel-arm64OK139
macos-oldrel-x86_64OK339
windows-develOK221
windows-releaseOK280
windows-oldrelOK261
wasm-releaseOK151

Exports:control.iglmcreate_userterms_skeletoniglmiglm.dataiglm.data_generatoriglm.object.generatorresultssampler.iglmsampler.net.attrsampler.net.attr.generatorsimulate_iglmstatistics

Dependencies:base64encclicodacpp11glueigraphjsonlitelatticelifecyclemagrittrMASSMatrixpkgconfigR6raggRcppRcppArmadilloRcppProgressrlangstringisystemfontstextshapingvctrs

Model Terms in iglm
Overview | Key Definitions | Category 1: Attribute Dependence Terms ($g_i$ Terms) | attribute_x | attribute_y | cov_x(data = v) | cov_y(data = v) | attribute_xy(mode = "global" | "local" | "alocal") | Category 2: Network Dependence Terms ($h_{i,j}$ Terms) | degrees | edges(mode = "global" | "local" | "alocal") | mutual(mode = "global" | "local" | "alocal") | cov_z(data = w, mode = "global" | "local" | "alocal") | cov_z_out(data = v, mode = "global" | "local" | "alocal") | cov_z_in(data = v, mode = "global" | "local" | "alocal") | isolates | nonisolates | gwdegree(mode = "global" | "local", decay = α) | gwodegree(mode = "global" | "local", decay = α) | gwidegree(mode = "global" | "local", decay = α) | transitive | gwesp_symm(mode = "global" | "local", decay = α) | gwesp(mode = "global" | "local", type = "OTP" | "ISP" | "OSP" | "ITP", decay = α) | gwdsp_symm(mode = "local", decay = α) | gwdsp(mode = "global" | "local", type = "OTP" | "ISP" | "OSP" | "ITP", decay = α) | Category 3: Joint Attribute/Network Dependence Terms ($h_{i,j}$ Terms) | attribute_xz(mode = "local") | attribute_yz(mode = "local") | edges_x_match(mode = "global" | "local") | edges_y_match(mode = "global" | "local") | outedges_x(mode = "global" | "local" | "alocal") | inedges_x(mode = "global" | "local" | "alocal") | outedges_y(mode = "global" | "local" | "alocal") | inedges_y(mode = "global" | "local" | "alocal") | spillover_xx(mode = "local") | spillover_xx_scaled(mode = "global" | "local") | spillover_yy(mode = "local") | spillover_yy_scaled(mode = "global" | "local") | spillover_xy(mode = "local") | spillover_xy_scaled(mode = "global" | "local") | spillover_yx(mode = "local") | spillover_yx_scaled(mode = "global" | "local") | spillover_yc(mode = "local", data = v) | Quick-Reference Table | Example: Combining Multiple Terms | References
Rendered frommodel-terms.Rmdusingknitr::rmarkdown

Last update: 2026-06-18
Started: 2026-06-18

Joint Modeling of Networks and Attributes with iglm
Overview | Model Specification | Model Simulation | Model Estimation | Model Assessment
Rendered fromiglm.Rmdusingknitr::rmarkdown

Last update: 2026-04-23
Started: 2025-11-10

Readme and manuals

Help Manual

Help pageTopics
Set Control Parameters for iglm Estimationcontrol.iglm
Copenhagen Network Studycopenhagen
Generate the Skeleton for an R Package to Implement Additional iglm Termscreate_userterms_skeleton
Construct an iglm Model Specification Objectiglm iglm.object
Model Specification for iglm Termsattribute_x-term attribute_xy-term attribute_xz-term attribute_y-term attribute_yz-term cov_x-term cov_y-term cov_z-term cov_z_in-term cov_z_out-term degrees-term edges-term edges_x_match-term edges_y_match-term gwdegree-term gwdsp-term gwesp-term gwidegree-term gwodegree-term iglm-terms iglm.terms inedges_x-term inedges_y-term isolates-term mutual-term nonisolates-term outedges_x-term outedges_y-term spillover_xx-term spillover_xx_scaled-term spillover_xy-term spillover_xy_scaled-term spillover_yc-term spillover_yc_symm-term spillover_yx-term spillover_yx_scaled-term spillover_yy-term spillover_yy_scaled-term transitive-term
Constructor for the iglm.data R6 objectiglm.data
Networks with Unit-Level Attributes (R6 Class)iglm.data_generator
iglm Objects (R6 Class)iglm.object.generator
Constructor for the results R6 Objectresults
iglm Estimation and Simulation Results (R6 Class)results.generator
Constructor for a iglm Samplersampler.iglm
iglm Sampler Settings (R6 Class)sampler.iglm.generator
Constructor for Single Component Sampler Settingssampler.net.attr
Single Component Sampler Settings (R6 Class)sampler.net.attr.generator
Simulate Responses and Connectionssimulate_iglm
Twitter (X) data list for U.S. state legislators (10-state subset)state_twitter
Compute Statisticsstatistics