BootstrapProperty()
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BootstrapProperty computes igraph analytics function on ensemble |
CreateIgGraphs()
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Convert a list of adjacency matrices to a list of igraph graphs. |
FitOmega()
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Fit propensity matrix for full model |
JnBlock()
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Fisher Information matrix for estimators in block models. |
RMSE()
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Computes the Root Mean Squared Error |
RMSLE()
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Computes the Root Mean Squared Logged Error |
adj2el()
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Maps adjacency matrix to edgelist |
adj_karate
|
Zachary's Karate Club graph |
as.ghype()
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Map list to ghype object |
bccm() print(<bccm>)
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Fitting bccm models |
checkGraphtype()
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Check graph input type (for whether it's a graph or a edgelist). |
coef(<nrm>)
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Extraction method for coefficients of models of class 'nrm' . |
compute_xi() ComputeXi()
|
Auxiliary function. Computes combinatorial matrix. |
conf.test()
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Test regular (gnp) vs configuration model |
highschool.predictors
|
Highschool contact network adjacency matrix |
cospons_mat
|
Swiss MPs network adjacency matrix |
coxsnellR2()
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Computes Cox and Snell pseudo R-squared for nrm models. |
create_predictors() createPredictors()
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Create a nrmpredictor object from passed argument |
create_predictors(<list>)
|
Create a nrmpredictor object from list |
dt
|
Swiss MPs attribute data frame. |
dtcommittee
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Swiss MPs committee affiliation data frame. |
el2adj()
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Maps edgelist to adjacency matrix |
extract.nrm.cluster()
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Extract details from statistical models for table construction. The function has methods for a range of statistical models. |
get_zero_dummy()
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Create a dummy variable to encode zero values of another variable. |
ghype() print(<ghype>)
|
Fitting gHypEG models |
gof.test()
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Perform a goodness-of-fit test |
highschool.multiplex
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Highschool contact network multiplex representation |
highschool.predictors
|
Highschool contact network predictors |
homophily_stat()
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Calculate homophily in multi-edge graphs. |
isNetwork()
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Test null model vs full ghype. |
linkSignificance() link_significance()
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Estimate statistical deviations from ghype model |
logLik(<ghype>)
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Extract Log-Likelihood |
logl()
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General method to compute log-likelihood for ghype models. |
loglratio()
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Compute log-likelihood ratio for ghype models. |
lr.test()
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Perform likelihood ratio test between two ghype models. |
mat2vec.ix()
|
Auxiliary function, gives mask for matrix for directed,
undirected etc. |
mcfaddenR2()
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Computes Mc Fadden pseudo R-squared. |
nr.ci()
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Confidence intervals for nrm models. |
nr.significance()
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Computes the significance of more complex model against a simpler model by
means of a likelihood ratio test. |
nrm() print(<nrm>)
|
Fitting gHypEG regression models for multi-edge networks. |
nrmChoose() nrm_choose()
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Selects the best set of predictors among the given sets by means of AIC. |
nrmSelection() nrm_selection() print(<nrm_selection>)
|
Perform AIC forward selection for nrm. |
onlinesim_mat
|
Swiss MPs committee similarity matrix. |
predict(<nrm>)
|
Method to predict the expected values of a nrm model |
reciprocity_stat()
|
Calculate weighted reciprocity change statistics for multi-edge graphs. |
regularm()
|
Fit the gnm model |
residuals(<nrm>)
|
Method to compute residuals of nrm models |
rghype()
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Generate random realisations from ghype model. |
scm()
|
Fit the Soft-Configuration Model |
sharedPartner_stat()
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Calculate (un-)weighted shared partner change statistics for multi-edge graphs. |
summary(<nrm>) print(<summary.nrm>)
|
Summary method for elements of class 'nrm' . |
summary(<nrm_selection>) print(<summary.nrm_selection>)
|
Summary method for elements of class 'nrm_selection' . |
vec2mat()
|
Auxiliary function, produces matrix from vector |
vertexlabels
|
Zachary's Karate Club vertex faction assignment |