Package: Bergm 5.0.7

Bergm: Bayesian Exponential Random Graph Models

Bayesian analysis for exponential random graph models using advanced computational algorithms. More information can be found at: <https://acaimo.github.io/Bergm/>.

Authors:Alberto Caimo [aut, cre], Lampros Bouranis [aut], Robert Krause [aut] Nial Friel [ctb]

Bergm_5.0.7.tar.gz
Bergm_5.0.7.zip(r-4.7)Bergm_5.0.7.zip(r-4.6)Bergm_5.0.7.zip(r-4.5)
Bergm_5.0.7.tgz(r-4.6-any)Bergm_5.0.7.tgz(r-4.5-any)
Bergm_5.0.7.tar.gz(r-4.7-any)Bergm_5.0.7.tar.gz(r-4.6-any)
Bergm_5.0.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
Bergm/json (API)

# Install 'Bergm' in R:

install.packages('Bergm', repos = c('https://acaimo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/acaimo/bergm/issues

Datasets:

lazega - Lazega lawyers network data

On CRAN:

6.26 score 17 stars 4 packages 37 scripts 4.8k downloads 8 exports 88 dependencies

Last updated from:800f85d34d. Checks:7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	WARNING	161
source / vignettes	OK	172
linux-release-x86_64	WARNING	148
macos-release-arm64	WARNING	149
macos-oldrel-arm64	WARNING	184
windows-devel	WARNING	105
windows-release	WARNING	104
windows-oldrel	WARNING	105
wasm-release	OK	125

Exports:bergm bergmC bergmM bgof ergmAPL evidence evidenceCJ evidencePP

Dependencies:backports bit bit64 boot broom cachem cli clipr coda codetools cpp11 crayon DEoptimR dplyr ergm evaluate fastmap forcats foreach generics glmnet glue haven highr hms iterators jomo knitr lattice lifecycle lme4 lpSolveAPI magrittr MASS Matrix matrixcalc MatrixModels mcmc MCMCpack memoise mice minqa mitml mvtnorm network nlme nloptr nnet numDeriv ordinal pan pillar pkgconfig prettyunits progress purrr quantreg R6 rbibutils Rcpp RcppEigen Rdpack readr reformulas Rglpk rlang rle robustbase rpart shape slam SparseM statnet.common stringi stringr survival tibble tidyr tidyselect trust tzdb ucminf utf8 vctrs vroom withr xfun yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Bayesian exponential random graph models	Bergm-package Bergm
Parameter estimation for Bayesian ERGMs	bergm
Calibrating misspecified Bayesian ERGMs	bergmC
Parameter estimation for Bayesian ERGMs under missing data	bergmM
Bayesian goodness-of-fit diagnostics for ERGMs	bgof
Adjustment of ERGM pseudolikelihood	ergmAPL
Wrapper function for evidence estimation	evidence
Evidence estimation via Chib and Jeliazkov's method	evidenceCJ
Evidence estimation via power posteriors	evidencePP
Lazega lawyers network data	lazega
Plot BERGM posterior output	plot.bergm
Summary of BERGM posterior output	summary.bergm