Auxiliary for Controlling ergm.bridge
Create a Simple Random network of a Given Size
Extract Number of parameters in ergm Model
Convert a network object into a numeric edgelist matrix
Ensures an Ergm Term and its Arguments Meet Appropriate Conditions
Extract Model Coefficients
ANOVA for ERGM Fits
approx.hotelling.diff.test
Approximate Hotelling T^2-Test for One Sample Means
Auxiliary for Controlling ERGM Fitting
Auxiliary for Controlling ERGM Goodness-of-Fit Evaluation
Auxiliary for Controlling SAN
Auxiliary for Controlling logLik.ergm
Computes and Returns the Degree Distribution Information for a Given Network
Auxiliary for Controlling ERGM Simulation
Two versions of an E. Coli network dataset
Convert a curved ERGM into a form suitable as initial values for the
same ergm.
Functions that will no longer be supported in future releases of the package
Internal Functions for Querying, Validating and Extracting from ERGM Formulas
Sample Space Constraints for Exponential-Family Random Graph Models
Internal ergm Objects
initializes the parameters to bound degree during sampling
Operations with 'eta' vector of canonical parameter values from ergm model
ergm.bridge.dindstart.llk
Bridge sampling to estiamte log-likelihood of an ERGM, using a
dyad-independent ERGM as a staring point.
Calculate the exact loglikelihood for an ERGM
Parallel Processing in the ergm
Testing for durational dependent models
Testing for curved exponential family
Reference Measures for Exponential-Family Random Graph Models
Exponential-Family Random Graph Models
Table mapping reference,constraints, etc. to Metropolis Hastings Proposals (MHP)
Goodreau's Faux Mesa High School as a network object
Create a network containing only edges meeting a specific criteria
Convert a curved ERGM into a corresponding ``fixed'' ERGM.
Conduct MCMC diagnostics on an ergm fit
A logLik ergm
Internal Function to Prepare Data for ergm's C Interface
Search the ergm-terms documentation for appropriate terms
Exponential Random Graph Models
Find a maximizer to the psuedolikelihood function
Checks an ergm Object for Degeneracy
Testing for dyad-independence
ERGM Predictors and response for logistic regression calculation of MPLE
Determine whether a vector is in the closure of the convex hull of
some sample of vectors
ergm.ConstraintImplications
Set up the implied constraints from the current constraint
A simple implementation of bridge sampling to evaluate
log-likelihood-ratio between two ERGM configurations
Set up the initial fitting methods for reference measure and
query available methods for that reference measure
Internal Function to Sample Networks Using C Wrapper
Goodreau's Faux Magnolia High School as a network object
Faux dixon High School as a network object
Plot Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model
Two-Dimensional Visualization of Networks
Replaces the sociomatrix in a network object
Internal function to create a new network from the ergm MCMC sample output
Extract Model Covariance Matrix
Calculation of network or graph statistics
Weighted Median
Metropolis-Hastings Proposal Methods for ERGM MCMC
Florentine Family Business Ties Data as a ``network" object
Fit, Simulate and Diagnose Exponential-Family Models for Networks
Florentine Family Marriage Ties Data as a ``network" object
Florentine Family Marriage and Business Ties Data as a ``network" object
Goodreau's four node network as a ``network" object
Storing last toggle information in a network
Kapferer's tailor shop data
Copy a network object enforcing ergm-appropriate guarantees about its internal representation
Summarizing ERGM Model Fits
Terms used in Exponential Family Random Graph Models
calculate geodesic distance distribution for a network or edgelist
Calculate all possible vectors of statistics on a network for an ERGM
internal function to return global statistics for a given network
Faux desert High School as a network object
Updating ergm.userterms
Getting Started with "ergm": Fit, simulate and diagnose exponential-family models for networks
utility operations for mcmc.list objects
Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model
Synthetic network with 20 nodes and 28 edges
Copy network- and vertex-level attributes between two network objects
Retrieve and check assumptions about vertex attributes (nodal covariates) in a network
Plotting Method for class ergm
Summaries the Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model
Summarizing network.list objects