# mlergm v0.1

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## Multilevel Exponential-Family Random Graph Models

Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (2006) <DOI: 10.1198/106186006X133069>. The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.

## Functions in mlergm

Name | Description | |

simulate_mlnet | Simulate a multilevel network | |

set_options | Set and adjust options and settings. | |

print.gof_mlergm | Print summary of a gof_mlergm object. | |

is.mlnet | Check if object is of class mlnet | |

mlergm | Multilevel Exponential-Family Random Graph Models | |

mlnet | Multilevel Network | |

plot.gof_mlergm | Plot goodness-of-fit results | |

is.gof_mlergm | Check if object is of class gof_mlergm | |

is.mlergm | Check if the object is of class mlergm | |

classes | Polish school classes data set. | |

gof.mlergm | Evaluate the goodness-of-fit of an estimated model. | |

No Results! |

## Vignettes of mlergm

Name | ||

mlergm_tutorial.Rmd | ||

vig_data.rda | ||

No Results! |

## Last month downloads

## Details

License | GPL-3 |

Encoding | UTF-8 |

LazyData | true |

RoxygenNote | 6.1.1 |

VignetteBuilder | knitr |

NeedsCompilation | no |

Packaged | 2018-11-28 22:39:17 UTC; jstew |

Repository | CRAN |

Date/Publication | 2018-12-03 12:10:03 UTC |

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