Package: ghypernet 1.1.2

ghypernet: Fit and Simulate Generalised Hypergeometric Ensembles of Graphs

Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG). To learn how to use it, check the vignettes for a quick tutorial. Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300> together with those relevant references from the one listed below. The package is based on the research developed at the Chair of Systems Design, ETH Zurich. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <doi:10.48550/arXiv.1607.02441>. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>. Casiraghi, G., (2017) <doi:10.48550/arXiv.1702.02048>. Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>. Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>. Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>. Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.

Authors:Giona Casiraghi [aut, cre], Vahan Nanumyan [aut], Laurence Brandenberger [ctb], Giacomo Vaccario [ctb]

ghypernet_1.1.2.tar.gz
ghypernet_1.1.2.zip(r-4.7-any)ghypernet_1.1.2.zip(r-4.6-any)ghypernet_1.1.2.zip(r-4.5-any)
ghypernet_1.1.2.tgz(r-4.6-any)ghypernet_1.1.2.tgz(r-4.5-any)
ghypernet_1.1.2.tar.gz(r-4.7-any)ghypernet_1.1.2.tar.gz(r-4.6-any)
ghypernet_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ghypernet/json (API)

# Install 'ghypernet' in R:
install.packages('ghypernet', repos = c('https://gi0na.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gi0na/r-ghypernet/issues

Pkgdown/docs site:https://ghyper.net

Datasets:

On CRAN:

Conda:

data-miningdata-sciencegraphsnetworknetwork-analysisrandom-graph-generationrandom-graphs

5.81 score 8 stars 27 scripts 418 downloads 41 exports 32 dependencies

Last updated from:a31e3e82aa. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK247
source / vignettesOK307
linux-releaseOK193
macos-releaseOK233
macos-oldrelOK208
windows-develOK128
windows-releaseOK95
windows-oldrelOK116
wasm-releaseOK149

Exports:adj2elas.ghypebccmBootstrapPropertycompute_xiComputeXiconf.testcoxsnellR2create_predictorsCreateIgGraphscreatePredictorsel2adjextract.nrm.clusterFitOmegaget_zero_dummyghypegof.testhomophily_statisNetworklink_significancelinkSignificanceloglloglratiolr.testmat2vec.ixmcfaddenR2nr.cinr.significancenrmnrm_choosenrm_selectionnrmChoosenrmSelectionreciprocity_statregularmrghypeRMSERMSLEscmsharedPartner_statvec2mat

Dependencies:askpassclicurldplyrgenericsgluehttrjsonlitelifecyclemagrittrmimenumbersopensslpbmcapplypillarpkgconfigplyrpurrrR6Rcppreshape2rlangrootSolvestringistringrsystexregtibbletidyselectutf8vctrswithr

An introduction to generalized hypergeometric ensembles (gHypEG) regressions.
Introduction | Part 1: Data preparation | Loading package and data | Network data manipulations | Compiling nodal attribute data | Independent and control variables | The role of zero-values in covariates | Change statistics to calculate endogenous network statistics | Reciprocity | Triadic closure | Homophily | Creating custom covariates | (Attribute-based) Degree measures | Creating the dummy variables to encode the zero values of each covariate | Part 2: Running gHypEG regressions | Regression set up (how to) | What are initial values? | Regression table export (using the texreg package) | Regression with degree correction (standard version) | Regression without degree correction (and when to use it) | Part 3: Model assessment, network simulations, gof | Model comparisons | Predicted probabilities | Network simulations
Rendered fromTutorial_NRM.Rmdusingknitr::rmarkdown

Last update: 2026-02-04
Started: 2019-09-02

Finding Significant Links with gHypEGs
Load Packages | Estimate the ensemble | Finding the significance of the links | Plot the results | Block Model
Rendered fromSignificantlinks.Rmdusingknitr::rmarkdown

Last update: 2021-10-15
Started: 2019-01-15

ghypernet Tutorial
Fitting a gHypEG Model | Block-Constrained Configuration Model | Goodness of Fit
Rendered fromtutorial.Rmdusingknitr::rmarkdown

Last update: 2019-02-01
Started: 2019-02-01

Readme and manuals

Help Manual

Help pageTopics
Zachary's Karate Club graphadj_karate
Maps adjacency matrix to edgelistadj2el
Map list to ghype objectas.ghype as.ghype.list as.ghype.nrm
Fitting bccm modelsbccm print.bccm
BootstrapProperty computes igraph analytics function on ensembleBootstrapProperty
Check graph input type (for whether it's a graph or a edgelist).checkGraphtype
Extraction method for coefficients of models of class ''nrm''.coef.nrm
Auxiliary function. Computes combinatorial matrix.ComputeXi compute_xi
Test regular (gnp) vs configuration modelconf.test
Highschool contact network adjacency matrixcontacts.adj
Swiss MPs network adjacency matrixcospons_mat
Computes Cox and Snell pseudo R-squared for nrm models.coxsnellR2
Create a nrmpredictor object from passed argumentcreatePredictors create_predictors
Create a nrmpredictor object from listcreate_predictors.list
Convert a list of adjacency matrices to a list of igraph graphs.CreateIgGraphs
Swiss MPs attribute data frame.dt
Swiss MPs committee affiliation data frame.dtcommittee
Maps edgelist to adjacency matrixel2adj
Extract details from statistical models for table construction. The function has methods for a range of statistical models.extract.nrm.cluster
Fit propensity matrix for full modelFitOmega
Create a dummy variable to encode zero values of another variable.get_zero_dummy
Fitting gHypEG modelsghype ghype.default ghype.igraph ghype.matrix print.ghype
Perform a goodness-of-fit testgof.test
Highschool contact network multiplex representationhighschool.multiplex
Highschool contact network predictorshighschool.predictors
Calculate homophily in multi-edge graphs.homophily_stat
Test null model vs full ghype.isNetwork
Fisher Information matrix for estimators in block models.JnBlock
Estimate statistical deviations from ghype modellinkSignificance link_significance
General method to compute log-likelihood for ghype models.logl logl.ghype logl.matrix
Extract Log-LikelihoodlogLik.ghype
Compute log-likelihood ratio for ghype models.loglratio
Perform likelihood ratio test between two ghype models.lr.test
Auxiliary function, gives mask for matrix for directed, undirected etc.mat2vec.ix
Computes Mc Fadden pseudo R-squared.mcfaddenR2
Confidence intervals for nrm models.nr.ci
Computes the significance of more complex model against a simpler model by means of a likelihood ratio test.nr.significance
Fitting gHypEG regression models for multi-edge networks.nrm nrm.default print.nrm
Selects the best set of predictors among the given sets by means of AIC.nrmChoose nrm_choose
Perform AIC forward selection for nrm.nrmSelection nrm_selection nrm_selection.default nrm_selection.nrmpredictor print.nrm_selection
Swiss MPs committee similarity matrix.onlinesim_mat
Method to predict the expected values of a nrm modelpredict.nrm
Calculate weighted reciprocity change statistics for multi-edge graphs.reciprocity_stat
Fit the gnm modelregularm
Method to compute residuals of nrm modelsresiduals.nrm
Generate random realisations from ghype model.rghype
Computes the Root Mean Squared ErrorRMSE
Computes the Root Mean Squared Logged ErrorRMSLE
Fit the Soft-Configuration Modelscm
Calculate (un-)weighted shared partner change statistics for multi-edge graphs.sharedPartner_stat
Summary method for elements of class ''nrm''.print.summary.nrm summary.nrm
Summary method for elements of class ''nrm_selection''.print.summary.nrm_selection summary.nrm_selection
Auxiliary function, produces matrix from vectorvec2mat
Zachary's Karate Club vertex faction assignmentvertexlabels