We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and inter-related, tasks involving exponential-family random graph models (ERGMs): estimation, simulation, and goodness of fit. More precisely, ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets from a fitted ERGM using Markov chain Monte Carlo; and assessing how well a fitted ERGM does at capturing characteristics of a particular network data set
Exponential-family random graph models are probabilistic network models that are parametrized by suf...
Exponential-family random graph models are probabilistic network models that are parametrized by suf...
Exponential-family random graph models are probabilistic network models that are parametrized by suf...
We describe some of the capabilities of the ergm package and the statistical theory underlying it. ...
We describe some of the capabilities of the ergm package and the statistical theory underlying it. ...
We describe some of the capabilities of the ergm package and the statistical theory underlying it. T...
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of...
Suggests lattice, latticeExtra, sna, Rglpk, snow, latentnet Description An integrated set of tools t...
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of ...
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of...
Exponential Family Random Graph Models (ERGM) are increasingly used in the study of social networks....
Exponential Family Random Graph Models (ERGM) are increasingly used in the study of social networks....
Exponential Family Random Graph Models (ERGM) are increasingly used in the study of social networks....
The ergm package supports the statistical analysis and simulation of network data. It anchors the st...
Markov chain Monte Carlo methods can be used to approximate the intractable normalizing constants th...
Exponential-family random graph models are probabilistic network models that are parametrized by suf...
Exponential-family random graph models are probabilistic network models that are parametrized by suf...
Exponential-family random graph models are probabilistic network models that are parametrized by suf...
We describe some of the capabilities of the ergm package and the statistical theory underlying it. ...
We describe some of the capabilities of the ergm package and the statistical theory underlying it. ...
We describe some of the capabilities of the ergm package and the statistical theory underlying it. T...
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of...
Suggests lattice, latticeExtra, sna, Rglpk, snow, latentnet Description An integrated set of tools t...
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of ...
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of...
Exponential Family Random Graph Models (ERGM) are increasingly used in the study of social networks....
Exponential Family Random Graph Models (ERGM) are increasingly used in the study of social networks....
Exponential Family Random Graph Models (ERGM) are increasingly used in the study of social networks....
The ergm package supports the statistical analysis and simulation of network data. It anchors the st...
Markov chain Monte Carlo methods can be used to approximate the intractable normalizing constants th...
Exponential-family random graph models are probabilistic network models that are parametrized by suf...
Exponential-family random graph models are probabilistic network models that are parametrized by suf...
Exponential-family random graph models are probabilistic network models that are parametrized by suf...