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amen (version 1.3)

Additive and Multiplicative Effects Models for Networks and Relational Data

Description

Analysis of dyadic network and relational data using additive and multiplicative effects (AME) models. The basic model includes regression terms, the covariance structure of the social relations model (Warner, Kenny and Stoto (1979) , Wong (1982) ), and multiplicative factor models (Hoff(2009) ). Four different link functions accommodate different relational data structures, including binary/network data (bin), normal relational data (nrm), ordinal relational data (ord) and data from fixed-rank nomination schemes (frn). Several of these link functions are discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) . Development of this software was supported in part by NIH grant R01HD067509.

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Version

Install

install.packages('amen')

Monthly Downloads

348

Version

1.3

License

GPL-3

Maintainer

Peter Hoff

Last Published

May 25th, 2017

Functions in amen (1.3)

normal relational data and covariates

ordinal relational data and covariates

International relations in the 90s

Linear combinations of submatrices of an array

binary relational data and covariates

binary relational data and covariates

row-specific ordinal relational data and covariates

AddHealth community 3 data

Censored binary nomination data and covariates

Fixed rank nomination data and covariates

AddHealth community 9 data

Network plotting

Plot results of an AME object

Edgelist to sociomatrix

Goodness of fit statistics

Computes the design socioarray of covariate values

Dutch college data

Gibbs sampling of U and V

Gibbs sampling of U and V

AME model fitting routine

AME model fitting routine for replicated relational data

Gibbs sampling of U and V

Additive and Multiplicative Effects Models for Networks and Relational Data

Circular network plot

Metropolis update for dyadic correlation with independent replicate data

Gibbs sampling of additive row and column effects and regression coefficient

rbeta_ab_rep_fc

Gibbs sampling of additive row and column effects and regression coefficient with independent replicate relational data

Sampson's monastery data

Sheep dominance data

Simulate Z given relative rank nomination data

Simulate a and Sab from full conditional distributions under bin likelihood

Gibbs update for dyadic variance

Sociomatrix to edgelist

Summary of an AME object

Simulate Z based on a probit model

Simulate missing values in a normal AME model

Simulate Z given the partial ranks

Simulate a network, i.e. a binary relational matrix

Lazega's law firm data

Symmetric square root of a matrix

Simulate a and Sab from full conditional distributions under the cbin likelihood

Simulate a and Sab from full conditional distributions under frn likelihood

Simulate an relational matrix based on a fixed rank nomination scheme

Simulate an relational matrix based on a relative rank nomination scheme

Simulate Z given its expectation and covariance

Simulate Z given fixed rank nomination data

Simulate Z given fixed rank nomination data

Simulation from a multivariate normal distribution

Metropolis update for dyadic correlation

Network embedding

rank-based z-scores

Gibbs update for dyadic variance with independent replicate relational data

Simulation from a Wishart distribution

Simulate a normal relational matrix

Simulate an ordinal relational matrix