Package: ergmito 0.3-1

George Vega Yon

ergmito: Exponential Random Graph Models for Small Networks

Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <doi:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm' package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms.

Authors:George Vega Yon [cre, aut], Kayla de la Haye [ths], Army Research Laboratory and the U.S. Army Research Office [fnd]

ergmito_0.3-1.tar.gz
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ergmito.pdf |ergmito.html✨
ergmito/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/muriteams/ergmito/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • fivenets - Example of a group of small networks

On CRAN:

ergmexponential-random-graph-modelsstatistics

27 exports 9 stars 1.47 score 47 dependencies 30 scripts 248 downloads

Last updated 1 years agofrom:13e68932db. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024
R-4.5-win-x86_64NOTESep 13 2024
R-4.5-linux-x86_64NOTESep 13 2024
R-4.4-win-x86_64OKSep 13 2024
R-4.4-mac-x86_64OKSep 13 2024
R-4.4-mac-aarch64OKSep 13 2024
R-4.3-win-x86_64OKSep 13 2024
R-4.3-mac-x86_64OKSep 13 2024
R-4.3-mac-aarch64OKSep 13 2024

Exports:as_adjmatAVAILABLE_STATSbenchmarkitoblockdiagonalizecount_statsergm_blockdiagergmitoergmito_bootergmito_formulaeexact_gradientexact_hessianexact_loglikextract.ergmitogeodesicgeodesitagof_ergmitoinduced_submatis_directedmatrix_to_networknedgesnew_rergmitonnetsnvertexpowersetrbernoullisame_distsplitnetwork

Dependencies:askpasscachemclicodacurlDEoptimRergmevaluatefansifastmapgluehighrhttrjsonliteknitrlatticelifecyclelpSolveAPImagrittrMASSMatrixmemoisemimenetworkopensslpillarpkgconfigpurrrR6rbibutilsRcppRcppArmadilloRdpackrlangrlerobustbasestatnet.commonstringistringrsystexregtibbletrustutf8vctrsxfunyaml

ERGM equations

George G Vega Yon

Rendered fromergm-equations.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2020-05-18
Started: 2020-01-13

Extending ergmito

George G. Vega Yon

Rendered fromextending-ergmito.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2020-04-10
Started: 2019-08-16

Readme and manuals

Help Manual

Help pageTopics
An alternative to 'as.matrix' to retrieve adjacency matrix fastas_adjmat
Utility to benchmark expression in Rbenchmarkito
Block-diagonal models using 'ergm'blockdiagonalize ergm_blockdiag splitnetwork
Check the convergence of ergmito estimatescheck_convergence check_support
Count Network StatisticsAVAILABLE_STATS count_stats count_stats.formula count_stats.list
Bootstrap of ergmitoergmito_boot
Processing formulas in 'ergmito'ergmito_formulae ergmito_loglik
Goodness of Fit diagnostics for ERGMito modelsergmito_gof gof_ergmito plot.ergmito_gof
Vectorized calculation of ERGM exact log-likelihoodexact_gradient exact_gradient.default exact_hessian exact_loglik exact_loglik.default
Extract function to be used with the 'texreg' package.extract.ergmito
Example of a group of small networksfivenets
Geodesic distance matrix (all pairs)geodesic geodesic.matrix geodesic.network geodesita
Extract a submatrix from a networkinduced_submat
Manipulation of network objectsmatrix_to_network matrix_to_network.matrix
Creates a new 'ergmito_ptr'new_ergmito_ptr
ERGMito samplernew_rergmito [.ergmito_sampler
Utility functions to query network dimensionsis_directed nedges nnets nvertex
Function to visualize the optimization surfaceplot.ergmito
Power set of Graphs of size 'n'powerset
Prediction method for 'ergmito' objectspredict.ergmito
Random Bernoulli graphrbernoulli
Compare pairs of networks to see if those came from the same distributionsame_dist
Draw samples from a fitted 'ergmito' modelsimulate.ergmito
Estimation of ERGMs using Maximum Likelihood Estimation (MLE)ergmito vcov.ergmito