In this paper we describe the main features of the Bergm package for the open-source Rsoftware which provides a comprehensive framework for Bayesian analysis of exponentialrandom graph models: tools for parameter estimation, model selection and goodness-of-t diagnostics. We illustrate the capabilities of this package describing the algorithmsthrough a tutorial analysis of three network datasets
The most promising class of statistical models for expressing structural properties of social networ...
R topics documented: Bergm-package....................................... 2 abergm.....................
Models with intractable likelihood functions arise in areas including network analysisand spatial st...
In this paper we describe the main features of the Bergm package for the open-source R software whic...
Recent advances in computational methods for intractable models have made network data increasingly ...
Exponential random graph models are a class of widely used exponential family models for social netw...
Networks (graphs) are broadly used to represent relations between entities in a wide range of scient...
The exponential random graph model (ERGM) is a class of stochastic models for network data widely ap...
Exponential random graph models are a class of widely used exponential fam-ily models for social net...
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of ...
We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal ra...
We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal ra...
We describe some of the capabilities of the ergm package and the statistical theory underlying it. ...
Bayesian inference for exponential random graph models Exponential random graph models are extremely...
The most promising class of statistical models for expressing structural properties of social networ...
R topics documented: Bergm-package....................................... 2 abergm.....................
Models with intractable likelihood functions arise in areas including network analysisand spatial st...
In this paper we describe the main features of the Bergm package for the open-source R software whic...
Recent advances in computational methods for intractable models have made network data increasingly ...
Exponential random graph models are a class of widely used exponential family models for social netw...
Networks (graphs) are broadly used to represent relations between entities in a wide range of scient...
The exponential random graph model (ERGM) is a class of stochastic models for network data widely ap...
Exponential random graph models are a class of widely used exponential fam-ily models for social net...
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of ...
We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal ra...
We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal ra...
We describe some of the capabilities of the ergm package and the statistical theory underlying it. ...
Bayesian inference for exponential random graph models Exponential random graph models are extremely...
The most promising class of statistical models for expressing structural properties of social networ...
R topics documented: Bergm-package....................................... 2 abergm.....................
Models with intractable likelihood functions arise in areas including network analysisand spatial st...
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