Fit, Simulate and Diagnose Exponential-Family Models for
Networks
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Documentation for package ‘ergm’ version 4.7.5
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A B C D E F G H I K L M N O P R S T U V W misc
-- A --
| absdiff-ergmTerm | Absolute difference in nodal attribute |
| absdiffcat-ergmTerm | Categorical absolute difference in nodal attribute |
| AIC.ergm | A 'logLik()' method for 'ergm' fits. |
| altkstar-ergmTerm | Alternating k-star |
| anova.ergm | ANOVA for ERGM Fits |
| anova.ergmlist | ANOVA for ERGM Fits |
| anyNA.ergm | Exponential-Family Random Graph Models |
| approx.hotelling.diff.test | Approximate Hotelling T^2-Test for One or Two Population Means |
| as.network.numeric | Create a Simple Random network of a Given Size |
| asymmetric-ergmTerm | Asymmetric dyads |
| atleast-ergmTerm | Number of dyads with values greater than or equal to a threshold |
| atmost-ergmTerm | Number of dyads with values less than or equal to a threshold |
| attr | Specifying nodal attributes and their levels |
| attrcov-ergmTerm | Edge covariate by attribute pairing |
| attrname | Specifying nodal attributes and their levels |
| attrs | Specifying nodal attributes and their levels |
-- B --
| B-ergmTerm | Wrap binary terms for use in valued models |
| b1concurrent-ergmTerm | Concurrent node count for the first mode in a bipartite network |
| b1cov-ergmTerm | Main effect of a covariate for the first mode in a bipartite network |
| b1degrange-ergmTerm | Degree range for the first mode in a bipartite network |
| b1degree-ergmTerm | Degree for the first mode in a bipartite network |
| b1degrees-ergmConstraint | Preserve the actor degree for bipartite networks |
| b1dsp-ergmTerm | Dyadwise shared partners for dyads in the first bipartition |
| b1factor-ergmTerm | Factor attribute effect for the first mode in a bipartite network |
| b1mindegree-ergmTerm | Minimum degree for the first mode in a bipartite network |
| b1nodematch-ergmTerm | Nodal attribute-based homophily effect for the first mode in a bipartite network |
| b1sociality-ergmTerm | Degree |
| b1star-ergmTerm | k-stars for the first mode in a bipartite network |
| b1starmix-ergmTerm | Mixing matrix for k-stars centered on the first mode of a bipartite network |
| b1twostar-ergmTerm | Two-star census for central nodes centered on the first mode of a bipartite network |
| b2concurrent-ergmTerm | Concurrent node count for the second mode in a bipartite network |
| b2cov-ergmTerm | Main effect of a covariate for the second mode in a bipartite network |
| b2degrange-ergmTerm | Degree range for the second mode in a bipartite network |
| b2degree-ergmTerm | Degree for the second mode in a bipartite network |
| b2degrees-ergmConstraint | Preserve the receiver degree for bipartite networks |
| b2dsp-ergmTerm | Dyadwise shared partners for dyads in the second bipartition |
| b2factor-ergmTerm | Factor attribute effect for the second mode in a bipartite network |
| b2mindegree-ergmTerm | Minimum degree for the second mode in a bipartite network |
| b2nodematch-ergmTerm | Nodal attribute-based homophily effect for the second mode in a bipartite network |
| b2sociality-ergmTerm | Degree |
| b2star-ergmTerm | k-stars for the second mode in a bipartite network |
| b2starmix-ergmTerm | Mixing matrix for k-stars centered on the second mode of a bipartite network |
| b2twostar-ergmTerm | Two-star census for central nodes centered on the second mode of a bipartite network |
| balance-ergmTerm | Balanced triads |
| bd-ergmConstraint | Constrain maximum and minimum vertex degree |
| Bernoulli-ergmReference | Bernoulli reference |
| BIC.ergm | A 'logLik()' method for 'ergm' fits. |
| blockdiag-ergmConstraint | Block-diagonal structure constraint |
| blocks-ergmConstraint | Constrain blocks of dyads defined by mixing type on a vertex attribute. |
| by | Specifying nodal attributes and their levels |
-- C --
| check.ErgmTerm | Ensures an Ergm Term and its Arguments Meet Appropriate Conditions |
| cohab | Target statistics and model fit to a hypothetical 50,000-node network population with 50,000 nodes based on egocent |
| cohab_MixMat | Target statistics and model fit to a hypothetical 50,000-node network population with 50,000 nodes based on egocent |
| cohab_PopWts | Target statistics and model fit to a hypothetical 50,000-node network population with 50,000 nodes based on egocent |
| cohab_TargetStats | Target statistics and model fit to a hypothetical 50,000-node network population with 50,000 nodes based on egocent |
| coincidence-ergmTerm | Coincident node count for the second mode in a bipartite (aka two-mode) network |
| COLLAPSE_SMALLEST | Specifying nodal attributes and their levels |
| concurrent-ergmTerm | Concurrent node count |
| concurrentties-ergmTerm | Concurrent tie count |
| constraints-ergm | Sample Space Constraints for Exponential-Family Random Graph Models |
| constraints.ergm | Sample Space Constraints for Exponential-Family Random Graph Models |
| control.ergm | Auxiliary function for fine-tuning ERGM fitting. |
| control.ergm.bridge | Auxiliaries for Controlling 'ergm.bridge.llr()' and 'logLik.ergm()' |
| control.gof | Auxiliary for Controlling ERGM Goodness-of-Fit Evaluation |
| control.gof.ergm | Auxiliary for Controlling ERGM Goodness-of-Fit Evaluation |
| control.gof.formula | Auxiliary for Controlling ERGM Goodness-of-Fit Evaluation |
| control.logLik.ergm | Auxiliaries for Controlling 'ergm.bridge.llr()' and 'logLik.ergm()' |
| control.san | Auxiliary for Controlling SAN |
| control.simulate | Auxiliary for Controlling ERGM Simulation |
| control.simulate.ergm | Auxiliary for Controlling ERGM Simulation |
| control.simulate.formula | Auxiliary for Controlling ERGM Simulation |
| control.simulate.formula.ergm | Auxiliary for Controlling ERGM Simulation |
| ctriad-ergmTerm | Cyclic triples |
| ctriple-ergmTerm | Cyclic triples |
| Curve-ergmTerm | Impose a curved structure on term parameters |
| cycle-ergmTerm | k-Cycle Census |
| cyclicalties-ergmTerm | Cyclical ties |
| cyclicalweights-ergmTerm | Cyclical weights |
-- D --
| ddsp-ergmTerm | Directed dyadwise shared partners |
| degcor-ergmTerm | Degree Correlation |
| degcrossprod-ergmTerm | Degree Cross-Product |
| degrange-ergmTerm | Degree range |
| degree-ergmTerm | Degree |
| degree1.5-ergmTerm | Degree to the 3/2 power |
| degreedist | Computes and Returns the Degree Distribution Information for a Given Network |
| degreedist-ergmConstraint | Preserve the degree distribution of the given network |
| degreedist.network | Computes and Returns the Degree Distribution Information for a Given Network |
| degrees-ergmConstraint | Preserve the degree of each vertex of the given network |
| density-ergmTerm | Density |
| desp-ergmTerm | Directed edgewise shared partners |
| deviance.ergm | A 'logLik()' method for 'ergm' fits. |
| dgwdsp-ergmTerm | Geometrically weighted dyadwise shared partner distribution |
| dgwesp-ergmTerm | Geometrically weighted edgewise shared partner distribution |
| dgwnsp-ergmTerm | Geometrically weighted non-edgewise shared partner distribution |
| diff-ergmTerm | Difference |
| DiscUnif-ergmReference | Discrete Uniform reference |
| dnsp-ergmTerm | Directed non-edgewise shared partners |
| dsp-ergmTerm | Directed dyadwise shared partners |
| dyadcov-ergmTerm | Dyadic covariate |
| dyadnoise-ergmConstraint | A soft constraint to adjust the sampled distribution for dyad-level noise with known perturbation probabilities |
| Dyads-ergmConstraint | Constrain fixed or varying dyad-independent terms |
-- E --
| ecoli | Two versions of an E. Coli network dataset |
| ecoli1 | Two versions of an E. Coli network dataset |
| ecoli2 | Two versions of an E. Coli network dataset |
| edgecov-ergmTerm | Edge covariate |
| edges-ergmConstraint | Preserve the edge count of the given network |
| edges-ergmTerm | Number of edges in the network |
| egocentric-ergmConstraint | Preserve values of dyads incident on vertices with given attribute |
| enformulate.curved | Convert a curved ERGM into a form suitable as initial values for the same ergm. Deprecated in 4.0.0. |
| enformulate.curved-deprecated | Convert a curved ERGM into a form suitable as initial values for the same ergm. Deprecated in 4.0.0. |
| enformulate.curved.ergm | Convert a curved ERGM into a form suitable as initial values for the same ergm. Deprecated in 4.0.0. |
| enformulate.curved.formula | Convert a curved ERGM into a form suitable as initial values for the same ergm. Deprecated in 4.0.0. |
| equalto-ergmTerm | Number of dyads with values equal to a specific value (within tolerance) |
| ergm | Exponential-Family Random Graph Models |
| ergm-constraints | Sample Space Constraints for Exponential-Family Random Graph Models |
| ergm-hints | MCMC Hints for Exponential-Family Random Graph Models |
| ergm-keywords | Keywords defined for Exponential-Family Random Graph Models |
| ergm-options | Global options and term options for the 'ergm' package |
| ergm-parallel | Parallel Processing in the 'ergm' Package |
| ergm-proposals | Metropolis-Hastings Proposal Methods for ERGM MCMC |
| ergm-references | Reference Measures for Exponential-Family Random Graph Models |
| ergm-terms | Terms used in Exponential Family Random Graph Models |
| ergm.allstats | Calculate all possible vectors of statistics on a network for an ERGM |
| ergm.bridge.0.llk | Bridge sampling to evaluate ERGM log-likelihoods and log-likelihood ratios |
| ergm.bridge.dindstart.llk | Bridge sampling to evaluate ERGM log-likelihoods and log-likelihood ratios |
| ergm.bridge.llr | Bridge sampling to evaluate ERGM log-likelihoods and log-likelihood ratios |
| ergm.constraints | Sample Space Constraints for Exponential-Family Random Graph Models |
| ergm.design | Obtain the set of informative dyads based on the network structure. |
| ergm.exact | Calculate all possible vectors of statistics on a network for an ERGM |
| ergm.getCluster | Parallel Processing in the 'ergm' Package |
| ergm.getnetwork | Acquire and verify the network from the LHS of an 'ergm' formula and verify that it is a valid network. |
| ergm.godfather | A function to apply a given series of changes to a network. |
| ergm.godfather.ergm_model | A function to apply a given series of changes to a network. |
| ergm.godfather.ergm_state | A function to apply a given series of changes to a network. |
| ergm.godfather.formula | A function to apply a given series of changes to a network. |
| ergm.hints | MCMC Hints for Exponential-Family Random Graph Models |
| ergm.keywords | Keywords defined for Exponential-Family Random Graph Models |
| ergm.object | Exponential-Family Random Graph Models |
| ergm.parallel | Parallel Processing in the 'ergm' Package |
| ergm.proposals | Metropolis-Hastings Proposal Methods for ERGM MCMC |
| ergm.references | Reference Measures for Exponential-Family Random Graph Models |
| ergm.restartCluster | Parallel Processing in the 'ergm' Package |
| ergm.stopCluster | Parallel Processing in the 'ergm' Package |
| ergm.terms | Terms used in Exponential Family Random Graph Models |
| ergmConstraint | Sample Space Constraints for Exponential-Family Random Graph Models |
| ergmHint | MCMC Hints for Exponential-Family Random Graph Models |
| ergmKeyword | Keywords defined for Exponential-Family Random Graph Models |
| ergmMPLE | ERGM Predictors and response for logistic regression calculation of MPLE |
| ergmProposal | Metropolis-Hastings Proposal Methods for ERGM MCMC |
| ergmReference | Reference Measures for Exponential-Family Random Graph Models |
| ergmTerm | Terms used in Exponential Family Random Graph Models |
| ergmTerm-options | Global options and term options for the 'ergm' package |
| ergm_MCMC_sample | Internal Function to Sample Networks and Network Statistics |
| ergm_MCMC_slave | Internal Function to Sample Networks and Network Statistics |
| ergm_plot.mcmc.list | Plot MCMC list using 'lattice' package graphics |
| ergm_state_cache | A rudimentary cache for large objects |
| ergm_symmetrize | Return a symmetrized version of a binary network |
| ergm_symmetrize.default | Return a symmetrized version of a binary network |
| ergm_symmetrize.network | Return a symmetrized version of a binary network |
| esp-ergmTerm | Directed edgewise shared partners |
| Exp-ergmTerm | Exponentiate a network's statistic |
-- F --
| F-ergmTerm | Filtering on arbitrary one-term model |
| faux.desert.high | Faux desert High School as a network object |
| faux.dixon.high | Faux dixon High School as a network object |
| faux.magnolia.high | Goodreau's Faux Magnolia High School as a network object |
| faux.mesa.high | Goodreau's Faux Mesa High School as a network object |
| fauxhigh | Goodreau's Faux Mesa High School as a network object |
| fix.curved | Convert a curved ERGM into a corresponding "fixed" ERGM. |
| fix.curved.ergm | Convert a curved ERGM into a corresponding "fixed" ERGM. |
| fix.curved.formula | Convert a curved ERGM into a corresponding "fixed" ERGM. |
| fixallbut-ergmConstraint | Preserve the dyad status in all but the given edges |
| fixedas-ergmConstraint | Fix specific dyads |
| flobusiness | Florentine Family Marriage and Business Ties Data as a "network" object |
| flomarriage | Florentine Family Marriage and Business Ties Data as a "network" object |
| florentine | Florentine Family Marriage and Business Ties Data as a "network" object |
| For-ergmTerm | A 'for' operator for terms |
-- G --
| g4 | Goodreau's four node network as a "network" object |
| get.MT_terms | Parallel Processing in the 'ergm' Package |
| geweke.diag.mv | Multivariate version of 'coda"s 'coda::geweke.diag()'. |
| gof | Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model |
| gof.default | Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model |
| gof.ergm | Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model |
| gof.formula | Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model |
| greaterthan-ergmTerm | Number of dyads with values strictly greater than a threshold |
| gwb1degree-ergmTerm | Geometrically weighted degree distribution for the first mode in a bipartite network |
| gwb1dsp-ergmTerm | Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition |
| gwb2degree-ergmTerm | Geometrically weighted degree distribution for the second mode in a bipartite network |
| gwb2dsp-ergmTerm | Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition |
| gwdegree-ergmTerm | Geometrically weighted degree distribution |
| gwdsp-ergmTerm | Geometrically weighted dyadwise shared partner distribution |
| gwesp-ergmTerm | Geometrically weighted edgewise shared partner distribution |
| gwidegree-ergmTerm | Geometrically weighted in-degree distribution |
| gwnsp-ergmTerm | Geometrically weighted non-edgewise shared partner distribution |
| gwodegree-ergmTerm | Geometrically weighted out-degree distribution |
-- H --
| hamming-ergmConstraint | Preserve the hamming distance to the given network (BROKEN: Do NOT Use) |
| hamming-ergmTerm | Hamming distance |
| hints | MCMC Hints for Exponential-Family Random Graph Models |
| hints-ergm | MCMC Hints for Exponential-Family Random Graph Models |
| hints.ergm | MCMC Hints for Exponential-Family Random Graph Models |
-- I --
| idegrange-ergmTerm | In-degree range |
| idegree-ergmTerm | In-degree |
| idegree1.5-ergmTerm | In-degree to the 3/2 power |
| idegreedist-ergmConstraint | Preserve the indegree distribution |
| idegrees-ergmConstraint | Preserve indegree for directed networks |
| ininterval-ergmTerm | Number of dyads whose values are in an interval |
| InitErgmConstraint..triadic | Network with strong clustering (triad-closure) effects |
| InitErgmConstraint.b1degrees | Preserve the actor degree for bipartite networks |
| InitErgmConstraint.b2degrees | Preserve the receiver degree for bipartite networks |
| InitErgmConstraint.bd | Constrain maximum and minimum vertex degree |
| InitErgmConstraint.blockdiag | Block-diagonal structure constraint |
| InitErgmConstraint.blocks | Constrain blocks of dyads defined by mixing type on a vertex attribute. |
| InitErgmConstraint.degreedist | Preserve the degree distribution of the given network |
| InitErgmConstraint.degrees | Preserve the degree of each vertex of the given network |
| InitErgmConstraint.dyadnoise | A soft constraint to adjust the sampled distribution for dyad-level noise with known perturbation probabilities |
| InitErgmConstraint.Dyads | Constrain fixed or varying dyad-independent terms |
| InitErgmConstraint.edges | Preserve the edge count of the given network |
| InitErgmConstraint.egocentric | Preserve values of dyads incident on vertices with given attribute |
| InitErgmConstraint.fixallbut | Preserve the dyad status in all but the given edges |
| InitErgmConstraint.fixedas | Fix specific dyads |
| InitErgmConstraint.hamming | Preserve the hamming distance to the given network (BROKEN: Do NOT Use) |
| InitErgmConstraint.idegreedist | Preserve the indegree distribution |
| InitErgmConstraint.idegrees | Preserve indegree for directed networks |
| InitErgmConstraint.nodedegrees | Preserve the degree of each vertex of the given network |
| InitErgmConstraint.observed | Preserve the observed dyads of the given network |
| InitErgmConstraint.odegreedist | Preserve the outdegree distribution |
| InitErgmConstraint.odegrees | Preserve outdegree for directed networks |
| InitErgmConstraint.sparse | Sparse network |
| InitErgmConstraint.strat | Stratify Proposed Toggles by Mixing Type on a Vertex Attribute |
| InitErgmConstraint.triadic | Network with strong clustering (triad-closure) effects |
| InitErgmProposal | Metropolis-Hastings Proposal Methods for ERGM MCMC |
| InitErgmReference.Bernoulli | Bernoulli reference |
| InitErgmReference.DiscUnif | Discrete Uniform reference |
| InitErgmReference.StdNormal | Standard Normal reference |
| InitErgmReference.Unif | Continuous Uniform reference |
| InitErgmTerm | Terms used in Exponential Family Random Graph Models |
| InitErgmTerm.absdiff | Absolute difference in nodal attribute |
| InitErgmTerm.absdiffcat | Categorical absolute difference in nodal attribute |
| InitErgmTerm.altkstar | Alternating k-star |
| InitErgmTerm.asymmetric | Asymmetric dyads |
| InitErgmTerm.attrcov | Edge covariate by attribute pairing |
| InitErgmTerm.b1concurrent | Concurrent node count for the first mode in a bipartite network |
| InitErgmTerm.b1cov | Main effect of a covariate for the first mode in a bipartite network |
| InitErgmTerm.b1degrange | Degree range for the first mode in a bipartite network |
| InitErgmTerm.b1degree | Degree for the first mode in a bipartite network |
| InitErgmTerm.b1dsp | Dyadwise shared partners for dyads in the first bipartition |
| InitErgmTerm.b1factor | Factor attribute effect for the first mode in a bipartite network |
| InitErgmTerm.b1mindegree | Minimum degree for the first mode in a bipartite network |
| InitErgmTerm.b1nodematch | Nodal attribute-based homophily effect for the first mode in a bipartite network |
| InitErgmTerm.b1sociality | Degree |
| InitErgmTerm.b1star | k-stars for the first mode in a bipartite network |
| InitErgmTerm.b1starmix | Mixing matrix for k-stars centered on the first mode of a bipartite network |
| InitErgmTerm.b1twostar | Two-star census for central nodes centered on the first mode of a bipartite network |
| InitErgmTerm.b2concurrent | Concurrent node count for the second mode in a bipartite network |
| InitErgmTerm.b2cov | Main effect of a covariate for the second mode in a bipartite network |
| InitErgmTerm.b2degrange | Degree range for the second mode in a bipartite network |
| InitErgmTerm.b2degree | Degree for the second mode in a bipartite network |
| InitErgmTerm.b2dsp | Dyadwise shared partners for dyads in the second bipartition |
| InitErgmTerm.b2factor | Factor attribute effect for the second mode in a bipartite network |
| InitErgmTerm.b2mindegree | Minimum degree for the second mode in a bipartite network |
| InitErgmTerm.b2nodematch | Nodal attribute-based homophily effect for the second mode in a bipartite network |
| InitErgmTerm.b2sociality | Degree |
| InitErgmTerm.b2star | k-stars for the second mode in a bipartite network |
| InitErgmTerm.b2starmix | Mixing matrix for k-stars centered on the second mode of a bipartite network |
| InitErgmTerm.b2twostar | Two-star census for central nodes centered on the second mode of a bipartite network |
| InitErgmTerm.balance | Balanced triads |
| InitErgmTerm.coincidence | Coincident node count for the second mode in a bipartite (aka two-mode) network |
| InitErgmTerm.concurrent | Concurrent node count |
| InitErgmTerm.concurrentties | Concurrent tie count |
| InitErgmTerm.ctriad | Cyclic triples |
| InitErgmTerm.ctriple | Cyclic triples |
| InitErgmTerm.Curve | Impose a curved structure on term parameters |
| InitErgmTerm.cycle | k-Cycle Census |
| InitErgmTerm.cyclicalties | Cyclical ties |
| InitErgmTerm.ddsp | Directed dyadwise shared partners |
| InitErgmTerm.degcor | Degree Correlation |
| InitErgmTerm.degcrossprod | Degree Cross-Product |
| InitErgmTerm.degrange | Degree range |
| InitErgmTerm.degree | Degree |
| InitErgmTerm.degree1.5 | Degree to the 3/2 power |
| InitErgmTerm.density | Density |
| InitErgmTerm.desp | Directed edgewise shared partners |
| InitErgmTerm.dgwdsp | Geometrically weighted dyadwise shared partner distribution |
| InitErgmTerm.dgwesp | Geometrically weighted edgewise shared partner distribution |
| InitErgmTerm.dgwnsp | Geometrically weighted non-edgewise shared partner distribution |
| InitErgmTerm.diff | Difference |
| InitErgmTerm.dnsp | Directed non-edgewise shared partners |
| InitErgmTerm.dsp | Directed dyadwise shared partners |
| InitErgmTerm.dyadcov | Dyadic covariate |
| InitErgmTerm.edgecov | Edge covariate |
| InitErgmTerm.edges | Number of edges in the network |
| InitErgmTerm.esp | Directed edgewise shared partners |
| InitErgmTerm.Exp | Exponentiate a network's statistic |
| InitErgmTerm.F | Filtering on arbitrary one-term model |
| InitErgmTerm.For | A 'for' operator for terms |
| InitErgmTerm.gwb1degree | Geometrically weighted degree distribution for the first mode in a bipartite network |
| InitErgmTerm.gwb1dsp | Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition |
| InitErgmTerm.gwb2degree | Geometrically weighted degree distribution for the second mode in a bipartite network |
| InitErgmTerm.gwb2dsp | Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition |
| InitErgmTerm.gwdegree | Geometrically weighted degree distribution |
| InitErgmTerm.gwdsp | Geometrically weighted dyadwise shared partner distribution |
| InitErgmTerm.gwesp | Geometrically weighted edgewise shared partner distribution |
| InitErgmTerm.gwidegree | Geometrically weighted in-degree distribution |
| InitErgmTerm.gwnsp | Geometrically weighted non-edgewise shared partner distribution |
| InitErgmTerm.gwodegree | Geometrically weighted out-degree distribution |
| InitErgmTerm.hamming | Hamming distance |
| InitErgmTerm.idegrange | In-degree range |
| InitErgmTerm.idegree | In-degree |
| InitErgmTerm.idegree1.5 | In-degree to the 3/2 power |
| InitErgmTerm.intransitive | Intransitive triads |
| InitErgmTerm.isolatededges | Isolated edges |
| InitErgmTerm.isolates | Isolates |
| InitErgmTerm.istar | In-stars |
| InitErgmTerm.kstar | k-stars |
| InitErgmTerm.Label | Modify terms' coefficient names |
| InitErgmTerm.localtriangle | Triangles within neighborhoods |
| InitErgmTerm.Log | Take a natural logarithm of a network's statistic |
| InitErgmTerm.m2star | Mixed 2-stars, a.k.a 2-paths |
| InitErgmTerm.meandeg | Mean vertex degree |
| InitErgmTerm.mm | Mixing matrix cells and margins |
| InitErgmTerm.mutual | Mutuality |
| InitErgmTerm.nearsimmelian | Near simmelian triads |
| InitErgmTerm.nodecov | Main effect of a covariate |
| InitErgmTerm.nodefactor | Factor attribute effect |
| InitErgmTerm.nodeicov | Main effect of a covariate for in-edges |
| InitErgmTerm.nodeifactor | Factor attribute effect for in-edges |
| InitErgmTerm.nodemain | Main effect of a covariate |
| InitErgmTerm.nodematch | Uniform homophily and differential homophily |
| InitErgmTerm.NodematchFilter | Filtering on nodematch |
| InitErgmTerm.nodemix | Nodal attribute mixing |
| InitErgmTerm.nodeocov | Main effect of a covariate for out-edges |
| InitErgmTerm.nodeofactor | Factor attribute effect for out-edges |
| InitErgmTerm.nsp | Directed non-edgewise shared partners |
| InitErgmTerm.odegrange | Out-degree range |
| InitErgmTerm.odegree | Out-degree |
| InitErgmTerm.odegree1.5 | Out-degree to the 3/2 power |
| InitErgmTerm.Offset | Terms with fixed coefficients |
| InitErgmTerm.opentriad | Open triads |
| InitErgmTerm.ostar | k-Outstars |
| InitErgmTerm.Parametrise | Impose a curved structure on term parameters |
| InitErgmTerm.Parametrize | Impose a curved structure on term parameters |
| InitErgmTerm.Prod | A product (or an arbitrary power combination) of one or more formulas |
| InitErgmTerm.receiver | Receiver effect |
| InitErgmTerm.S | Evaluation on an induced subgraph |
| InitErgmTerm.sender | Sender effect |
| InitErgmTerm.simmelian | Simmelian triads |
| InitErgmTerm.simmelianties | Ties in simmelian triads |
| InitErgmTerm.smalldiff | Number of ties between actors with similar attribute values |
| InitErgmTerm.sociality | Undirected degree |
| InitErgmTerm.Sum | A sum (or an arbitrary linear combination) of one or more formulas |
| InitErgmTerm.Symmetrize | Evaluation on symmetrized (undirected) network |
| InitErgmTerm.threepath | Three-trails |
| InitErgmTerm.threetrail | Three-trails |
| InitErgmTerm.transitive | Transitive triads |
| InitErgmTerm.transitiveties | Transitive ties |
| InitErgmTerm.triadcensus | Triad census |
| InitErgmTerm.triangle | Triangles |
| InitErgmTerm.tripercent | Triangle percentage |
| InitErgmTerm.ttriad | Transitive triples |
| InitErgmTerm.ttriple | Transitive triples |
| InitErgmTerm.twopath | 2-Paths |
| InitErgmWtTerm | Terms used in Exponential Family Random Graph Models |
| InitWtErgmProposal | Metropolis-Hastings Proposal Methods for ERGM MCMC |
| InitWtErgmTerm.absdiff | Absolute difference in nodal attribute |
| InitWtErgmTerm.absdiffcat | Categorical absolute difference in nodal attribute |
| InitWtErgmTerm.atleast | Number of dyads with values greater than or equal to a threshold |
| InitWtErgmTerm.atmost | Number of dyads with values less than or equal to a threshold |
| InitWtErgmTerm.B | Wrap binary terms for use in valued models |
| InitWtErgmTerm.b1cov | Main effect of a covariate for the first mode in a bipartite network |
| InitWtErgmTerm.b1factor | Factor attribute effect for the first mode in a bipartite network |
| InitWtErgmTerm.b1sociality | Degree |
| InitWtErgmTerm.b2cov | Main effect of a covariate for the second mode in a bipartite network |
| InitWtErgmTerm.b2factor | Factor attribute effect for the second mode in a bipartite network |
| InitWtErgmTerm.b2sociality | Degree |
| InitWtErgmTerm.Curve | Impose a curved structure on term parameters |
| InitWtErgmTerm.cyclicalties | Cyclical ties |
| InitWtErgmTerm.cyclicalweights | Cyclical weights |
| InitWtErgmTerm.diff | Difference |
| InitWtErgmTerm.edgecov | Edge covariate |
| InitWtErgmTerm.edges | Number of edges in the network |
| InitWtErgmTerm.equalto | Number of dyads with values equal to a specific value (within tolerance) |
| InitWtErgmTerm.Exp | Exponentiate a network's statistic |
| InitWtErgmTerm.greaterthan | Number of dyads with values strictly greater than a threshold |
| InitWtErgmTerm.ininterval | Number of dyads whose values are in an interval |
| InitWtErgmTerm.Label | Modify terms' coefficient names |
| InitWtErgmTerm.Log | Take a natural logarithm of a network's statistic |
| InitWtErgmTerm.match | Uniform homophily and differential homophily |
| InitWtErgmTerm.mm | Mixing matrix cells and margins |
| InitWtErgmTerm.mutual | Mutuality |
| InitWtErgmTerm.nodecov | Main effect of a covariate |
| InitWtErgmTerm.nodecovar | Covariance of undirected dyad values incident on each actor |
| InitWtErgmTerm.nodefactor | Factor attribute effect |
| InitWtErgmTerm.nodeicov | Main effect of a covariate for in-edges |
| InitWtErgmTerm.nodeicovar | Covariance of in-dyad values incident on each actor |
| InitWtErgmTerm.nodeifactor | Factor attribute effect for in-edges |
| InitWtErgmTerm.nodemain | Main effect of a covariate |
| InitWtErgmTerm.nodematch | Uniform homophily and differential homophily |
| InitWtErgmTerm.nodemix | Nodal attribute mixing |
| InitWtErgmTerm.nodeocov | Main effect of a covariate for out-edges |
| InitWtErgmTerm.nodeocovar | Covariance of out-dyad values incident on each actor |
| InitWtErgmTerm.nodeofactor | Factor attribute effect for out-edges |
| InitWtErgmTerm.nonzero | Number of edges in the network |
| InitWtErgmTerm.Parametrise | Impose a curved structure on term parameters |
| InitWtErgmTerm.Parametrize | Impose a curved structure on term parameters |
| InitWtErgmTerm.Prod | A product (or an arbitrary power combination) of one or more formulas |
| InitWtErgmTerm.receiver | Receiver effect |
| InitWtErgmTerm.sender | Sender effect |
| InitWtErgmTerm.smallerthan | Number of dyads with values strictly smaller than a threshold |
| InitWtErgmTerm.sociality | Undirected degree |
| InitWtErgmTerm.Sum | A sum (or an arbitrary linear combination) of one or more formulas |
| InitWtErgmTerm.sum | Sum of dyad values (optionally taken to a power) |
| InitWtErgmTerm.transitiveweights | Transitive weights |
| intransitive-ergmTerm | Intransitive triads |
| is.curved | Testing for curved exponential family |
| is.curved.ergm | Testing for curved exponential family |
| is.curved.formula | Testing for curved exponential family |
| is.curved.NULL | Testing for curved exponential family |
| is.dyad.independent | Testing for dyad-independence |
| is.dyad.independent.ergm | Testing for dyad-independence |
| is.dyad.independent.ergm_conlist | Testing for dyad-independence |
| is.dyad.independent.formula | Testing for dyad-independence |
| is.dyad.independent.NULL | Testing for dyad-independence |
| is.ergm | Exponential-Family Random Graph Models |
| is.na.ergm | Exponential-Family Random Graph Models |
| is.valued | Function to check whether an ERGM fit or some aspect of it is valued |
| is.valued.edgelist | Function to check whether an ERGM fit or some aspect of it is valued |
| is.valued.ergm | Function to check whether an ERGM fit or some aspect of it is valued |
| is.valued.ergm_state | Function to check whether an ERGM fit or some aspect of it is valued |
| is.valued.network | Function to check whether an ERGM fit or some aspect of it is valued |
| isolatededges-ergmTerm | Isolated edges |
| isolates-ergmTerm | Isolates |
| istar-ergmTerm | In-stars |
-- K --
| kapferer | Kapferer's tailor shop data |
| kapferer2 | Kapferer's tailor shop data |
| keywords-ergm | Keywords defined for Exponential-Family Random Graph Models |
| keywords.ergm | Keywords defined for Exponential-Family Random Graph Models |
| kstar-ergmTerm | k-stars |
-- L --
| Label-ergmTerm | Modify terms' coefficient names |
| LARGEST | Specifying nodal attributes and their levels |
| localtriangle-ergmTerm | Triangles within neighborhoods |
| Log-ergmTerm | Take a natural logarithm of a network's statistic |
| logLik.ergm | A 'logLik()' method for 'ergm' fits. |
| logLikNull | Calculate the null model likelihood |
| logLikNull.ergm | Calculate the null model likelihood |
-- M --
| m2star-ergmTerm | Mixed 2-stars, a.k.a 2-paths |
| match-ergmTerm | Uniform homophily and differential homophily |
| mcmc.diagnostics | Conduct MCMC diagnostics on a model fit |
| mcmc.diagnostics.default | Conduct MCMC diagnostics on a model fit |
| mcmc.diagnostics.ergm | Conduct MCMC diagnostics on a model fit |
| meandeg-ergmTerm | Mean vertex degree |
| mm-ergmTerm | Mixing matrix cells and margins |
| molecule | Synthetic network with 20 nodes and 28 edges |
| mutual-ergmTerm | Mutuality |
-- N --
| nearsimmelian-ergmTerm | Near simmelian triads |
| network.list | A convenience container for a list of 'network' objects, output by 'simulate.ergm()' among others. |
| nobs.ergm | Exponential-Family Random Graph Models |
| nodal.attr | Specifying nodal attributes and their levels |
| nodal.attribute | Specifying nodal attributes and their levels |
| nodal_attributes | Specifying nodal attributes and their levels |
| node.attr | Specifying nodal attributes and their levels |
| node.attribute | Specifying nodal attributes and their levels |
| nodecov-ergmTerm | Main effect of a covariate |
| nodecovar-ergmTerm | Covariance of undirected dyad values incident on each actor |
| nodedegrees-ergmConstraint | Preserve the degree of each vertex of the given network |
| nodefactor-ergmTerm | Factor attribute effect |
| nodeicov-ergmTerm | Main effect of a covariate for in-edges |
| nodeicovar-ergmTerm | Covariance of in-dyad values incident on each actor |
| nodeifactor-ergmTerm | Factor attribute effect for in-edges |
| nodeisqrtcovar-ergmTerm | Covariance of in-dyad values incident on each actor |
| nodemain-ergmTerm | Main effect of a covariate |
| nodematch-ergmTerm | Uniform homophily and differential homophily |
| NodematchFilter-ergmTerm | Filtering on nodematch |
| nodemix-ergmTerm | Nodal attribute mixing |
| nodeocov-ergmTerm | Main effect of a covariate for out-edges |
| nodeocovar-ergmTerm | Covariance of out-dyad values incident on each actor |
| nodeofactor-ergmTerm | Factor attribute effect for out-edges |
| nodesqrtcovar-ergmTerm | Covariance of undirected dyad values incident on each actor |
| nonzero-ergmTerm | Number of edges in the network |
| nparam | Length of the parameter vector associated with an object or with its terms. |
| nparam.default | Length of the parameter vector associated with an object or with its terms. |
| nparam.ergm | Length of the parameter vector associated with an object or with its terms. |
| nsp-ergmTerm | Directed non-edgewise shared partners |
| nthreads | Parallel Processing in the 'ergm' Package |
| nthreads.cluster | Parallel Processing in the 'ergm' Package |
| nthreads.control.list | Parallel Processing in the 'ergm' Package |
| nthreads.NULL | Parallel Processing in the 'ergm' Package |
-- O --
| observed-ergmConstraint | Preserve the observed dyads of the given network |
| odegrange-ergmTerm | Out-degree range |
| odegree-ergmTerm | Out-degree |
| odegree1.5-ergmTerm | Out-degree to the 3/2 power |
| odegreedist-ergmConstraint | Preserve the outdegree distribution |
| odegrees-ergmConstraint | Preserve outdegree for directed networks |
| Offset-ergmTerm | Terms with fixed coefficients |
| on | Specifying nodal attributes and their levels |
| opentriad-ergmTerm | Open triads |
| ostar-ergmTerm | k-Outstars |
-- P --
| parallel | Parallel Processing in the 'ergm' Package |
| parallel-ergm | Parallel Processing in the 'ergm' Package |
| parallel.ergm | Parallel Processing in the 'ergm' Package |
| Parametrise-ergmTerm | Impose a curved structure on term parameters |
| Parametrize-ergmTerm | Impose a curved structure on term parameters |
| param_names | Names of the parameters associated with an object. |
| param_names.default | Names of the parameters associated with an object. |
| param_names<- | Names of the parameters associated with an object. |
| plot.gof | Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model |
| predict.ergm | ERGM-based tie probabilities |
| predict.formula | ERGM-based tie probabilities |
| print.ergm | Exponential-Family Random Graph Models |
| print.gof | Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model |
| print.network.list | A convenience container for a list of 'network' objects, output by 'simulate.ergm()' among others. |
| print.summary.ergm | Summarizing ERGM Model Fits |
| Prod-ergmTerm | A product (or an arbitrary power combination) of one or more formulas |
| proposals-ergm | Metropolis-Hastings Proposal Methods for ERGM MCMC |
| proposals.ergm | Metropolis-Hastings Proposal Methods for ERGM MCMC |
-- R --
| rank_test.ergm | A lack-of-fit test for ERGMs |
| receiver-ergmTerm | Receiver effect |
| references-ergm | Reference Measures for Exponential-Family Random Graph Models |
| references.ergm | Reference Measures for Exponential-Family Random Graph Models |
-- S --
| S-ergmTerm | Evaluation on an induced subgraph |
| samplike | Cumulative network of positive affection within a monastery as a "network" object |
| samplk | Longitudinal networks of positive affection within a monastery as a "network" object |
| samplk1 | Longitudinal networks of positive affection within a monastery as a "network" object |
| samplk2 | Longitudinal networks of positive affection within a monastery as a "network" object |
| samplk3 | Longitudinal networks of positive affection within a monastery as a "network" object |
| sampson | Cumulative network of positive affection within a monastery as a "network" object |
| san | Generate networks with a given set of network statistics |
| san.default | Generate networks with a given set of network statistics |
| san.ergm_model | Generate networks with a given set of network statistics |
| san.formula | Generate networks with a given set of network statistics |
| search.ergmConstraints | Search ERGM terms, constraints, references, hints, and proposals |
| search.ergmHints | Search ERGM terms, constraints, references, hints, and proposals |
| search.ergmProposals | Search ERGM terms, constraints, references, hints, and proposals |
| search.ergmReferences | Search ERGM terms, constraints, references, hints, and proposals |
| search.ergmTerms | Search ERGM terms, constraints, references, hints, and proposals |
| sender-ergmTerm | Sender effect |
| set.MT_terms | Parallel Processing in the 'ergm' Package |
| simmelian-ergmTerm | Simmelian triads |
| simmelianties-ergmTerm | Ties in simmelian triads |
| simulate.ergm | Draw from the distribution of an Exponential Family Random Graph Model |
| simulate.ergm_model | Draw from the distribution of an Exponential Family Random Graph Model |
| simulate.ergm_state | Draw from the distribution of an Exponential Family Random Graph Model |
| simulate.ergm_state_full | Draw from the distribution of an Exponential Family Random Graph Model |
| simulate.formula | A 'simulate' Method for 'formula' objects that dispatches based on the Left-Hand Side |
| simulate.formula.ergm | Draw from the distribution of an Exponential Family Random Graph Model |
| simulate.formula_lhs | A 'simulate' Method for 'formula' objects that dispatches based on the Left-Hand Side |
| simulate.formula_lhs_network | Draw from the distribution of an Exponential Family Random Graph Model |
| simulate_formula | Draw from the distribution of an Exponential Family Random Graph Model |
| simulate_formula.ergm_state | Draw from the distribution of an Exponential Family Random Graph Model |
| simulate_formula.network | Draw from the distribution of an Exponential Family Random Graph Model |
| smalldiff-ergmTerm | Number of ties between actors with similar attribute values |
| smallerthan-ergmTerm | Number of dyads with values strictly smaller than a threshold |
| SMALLEST | Specifying nodal attributes and their levels |
| snctrl | Statnet Control |
| sociality-ergmTerm | Undirected degree |
| sparse-ergmConstraint | Sparse network |
| sparse-ergmHint | Sparse network |
| spectrum0.mvar | Multivariate version of 'coda"s 'spectrum0.ar()'. |
| StdNormal-ergmReference | Standard Normal reference |
| strat-ergmConstraint | Stratify Proposed Toggles by Mixing Type on a Vertex Attribute |
| strat-ergmHint | Stratify Proposed Toggles by Mixing Type on a Vertex Attribute |
| Sum-ergmTerm | A sum (or an arbitrary linear combination) of one or more formulas |
| sum-ergmTerm | Sum of dyad values (optionally taken to a power) |
| summary | Calculation of network or graph statistics or other attributes specified on a formula |
| summary.ergm | Summarizing ERGM Model Fits |
| summary.formula | Calculation of network or graph statistics or other attributes specified on a formula |
| summary.network.list | A convenience container for a list of 'network' objects, output by 'simulate.ergm()' among others. |
| Symmetrize-ergmTerm | Evaluation on symmetrized (undirected) network |
-- T --
| tailor | Kapferer's tailor shop data |
| term.options | Global options and term options for the 'ergm' package |
| terms-ergm | Terms used in Exponential Family Random Graph Models |
| terms.ergm | Terms used in Exponential Family Random Graph Models |
| threepath-ergmTerm | Three-trails |
| threetrail-ergmTerm | Three-trails |
| transitive-ergmTerm | Transitive triads |
| transitiveties-ergmTerm | Transitive ties |
| transitiveweights-ergmTerm | Transitive weights |
| triadcensus-ergmTerm | Triad census |
| triadic-ergmConstraint | Network with strong clustering (triad-closure) effects |
| triadic-ergmHint | Network with strong clustering (triad-closure) effects |
| triangle-ergmTerm | Triangles |
| triangles-ergmTerm | Triangles |
| tripercent-ergmTerm | Triangle percentage |
| ttriad-ergmTerm | Transitive triples |
| ttriple-ergmTerm | Transitive triples |
| twopath-ergmTerm | 2-Paths |
-- U --
| Unif-ergmReference | Continuous Uniform reference |
| update.network | Update the edges in a network based on a matrix |
| update_network | Update the edges in a network based on a matrix |
| update_network.data.frame | Update the edges in a network based on a matrix |
| update_network.ergm_state | Update the edges in a network based on a matrix |
| update_network.matrix | Update the edges in a network based on a matrix |
| update_network.matrix_edgelist | Update the edges in a network based on a matrix |
-- V --
| vcov.ergm | Exponential-Family Random Graph Models |
| vertex.attr | Specifying nodal attributes and their levels |
| vertex.attribute | Specifying nodal attributes and their levels |
-- W --
| wtd.median | Weighted Median |
-- misc --
| .dyads-ergmConstraint | A meta-constraint indicating handling of arbitrary dyadic constraints |
| .simulate_formula.network | Draw from the distribution of an Exponential Family Random Graph Model |
| .triadic-ergmHint | Network with strong clustering (triad-closure) effects |