In this paper we describe the main featuress of the Bergm package for the open-source R software which provides a comprehensive framework for Bayesian analysis for exponential random graph models: tools for parameter estimation, model selection and goodness-of-fit diagnostics. We illustrate the capabilities of this package describing the algorithms through a tutorial analysis of three network datasets
Models with intractable likelihood functions arise in areas including network analysisand spatial st...
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...
In this paper we describe the main features of the Bergm package for the open-source Rsoftware which...
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...
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...
Exponential random graph models are a class of widely used exponential fam-ily models for social net...
The exponential random graph model (ERGM) is a class of stochastic models for network data widely ap...
R topics documented: Bergm-package....................................... 2 abergm.....................
The most promising class of statistical models for expressing structural properties of social networ...
Models with intractable likelihood functions arise in areas including network analysisand spatial st...
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...
In this paper we describe the main features of the Bergm package for the open-source Rsoftware which...
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...
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...
Exponential random graph models are a class of widely used exponential fam-ily models for social net...
The exponential random graph model (ERGM) is a class of stochastic models for network data widely ap...
R topics documented: Bergm-package....................................... 2 abergm.....................
The most promising class of statistical models for expressing structural properties of social networ...
Models with intractable likelihood functions arise in areas including network analysisand spatial st...
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...