Package: iglm 1.2.4
Cornelius Fritz
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:
iglm_1.2.4.tar.gz
iglm_1.2.4.tar.gz(r-4.7-arm64)iglm_1.2.4.tar.gz(r-4.7-x86_64)iglm_1.2.4.tar.gz(r-4.6-arm64)iglm_1.2.4.tar.gz(r-4.6-x86_64)
iglm_1.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
iglm/json (API)
| # Install 'iglm' in R: |
| install.packages('iglm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- copenhagen - Copenhagen Network Study
- state_twitter - Twitter
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:d9bb162a19. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 194 | ||
| linux-devel-x86_64 | OK | 233 | ||
| source / vignettes | OK | 305 | ||
| linux-release-arm64 | OK | 200 | ||
| linux-release-x86_64 | OK | 230 | ||
| wasm-release | OK | 164 |
Exports:control.iglmcreate_userterms_skeletoniglmiglm.dataiglm.data_generatoriglm.object.generatorresultssampler.iglmsampler.net.attrsampler.net.attr.generatorsimulate_iglmstatistics
Dependencies:base64encclicodacpp11glueigraphjsonlitelatticelifecyclemagrittrMASSMatrixpkgconfigR6raggRcppRcppArmadilloRcppProgressrlangstringisystemfontstextshapingvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Set Control Parameters for iglm Estimation | control.iglm |
| Copenhagen Network Study | copenhagen |
| Generate the Skeleton for an R Package to Implement Additional iglm Terms | create_userterms_skeleton |
| Construct an iglm Model Specification Object | iglm iglm.object |
| Model Specification for iglm Terms | attribute_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 object | iglm.data |
| Networks with Unit-Level Attributes (R6 Class) | iglm.data_generator |
| iglm Objects (R6 Class) | iglm.object.generator |
| Constructor for the results R6 Object | results |
| iglm Estimation and Simulation Results (R6 Class) | results.generator |
| Constructor for a iglm Sampler | sampler.iglm |
| iglm Sampler Settings (R6 Class) | sampler.iglm.generator |
| Constructor for Single Component Sampler Settings | sampler.net.attr |
| Single Component Sampler Settings (R6 Class) | sampler.net.attr.generator |
| Simulate Responses and Connections | simulate_iglm |
| Twitter (X) data list for U.S. state legislators (10-state subset) | state_twitter |
| Compute Statistics | statistics |
