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degreenet (version 1.3-1)

Models for Skewed Count Distributions Relevant to Networks

Description

Likelihood-based inference for skewed count distributions used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.

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Version

Install

install.packages('degreenet')

Monthly Downloads

345

Version

1.3-1

License

GPL-3 + file LICENSE

Maintainer

Mark S Handcock

Last Published

April 19th, 2015

Functions in degreenet (1.3-1)

Discrete version of q-Exponential Modeling of Discrete Data

Poisson Lognormal Modeling of Discrete Data

Conway Maxwell Poisson Modeling of Discrete Data

Rounded Poisson Lognormal Modeling of Discrete Data

Simulate from a Yule Distribution

Calculate Bootstrap Estimates and Confidence Intervals for the Waring Distribution

Models for Count Distributions

Calculate Bootstrap Estimates and Confidence Intervals for the Discrete Pareto Distribution

Simulate from a Discrete Pareto Distribution

Waring Modeling of Discrete Data

Calculate the log-likelihood for Count Distributions

Simulate from a Conway Maxwell Poisson Distribution

Calculate Bootstrap Estimates and Confidence Intervals for the Negative Binomial Distribution

Generate a (non-random) network with a given degree sequence

degreenet-internal

Internal degreenet Objects

Calculate the Conditional log-likelihood for Count Distributions

Calculate Bootstrap Estimates and Confidence Intervals for the Poisson Lognormal Distribution

Calculate Bootstrap Estimates and Confidence Intervals for the Yule Distribution

degreenet-package

Models for Skewed Count Distributions Relevant to Networks

Generate a (non-random) network from a Yule Distribution

Calculate the log-likelihood for Count Distributions

Calculate the Conditional log-likelihood for the Poisson Lognormal Distributions

Simulate from a Waring Distribution

Simulate from a Poisson Lognormal Distribution

Number of sex partners in the last 12 months for men and women in Sweden

Simulate from a Negative Binomial Distribution

Yule Distribution Modeling of Discrete Data

Calculate the Conditional log-likelihood for Count Distributions