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amen (version 1.4.5)

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) ). Several different link functions accommodate different relational data structures, including binary/network data, normal relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from fixed-rank nomination schemes. 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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install.packages('amen')

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541

Version

1.4.5

License

GPL-3

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Repository

https://github.com/pdhoff/amen

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Last Published

February 20th, 2024

Functions in amen (1.4.5)

Computes the design socioarray of covariate values

Dutch college data

AddHealth community 9 data

Precomputation of design matrix quantities.

Gibbs update for additive effects covariance

Network plotting

Plot results of an AME object

Simulate Z given fixed rank nomination data

AME model fitting routine

SRM log likelihood evaluated on a grid of rho-values

Simulate Z based on a probit model

Simulate Z based on a tobit model

Additive and Multiplicative Effects Models for Networks and Relational Data

Simulate Z given fixed rank nomination data

Metropolis update for dyadic correlation

Metropolis update for dyadic correlation with independent replicate data

Simulate a and Sab from full conditional distributions under bin likelihood

Simulate an relational matrix based on a fixed rank nomination scheme

Simulate a normal relational matrix

Circular network plot

Sheep dominance data

Sociomatrix to edgelist

Summary of an AME object

Simulate missing values in a normal AME model

Simulate an ordinal relational matrix

AME model fitting routine for replicated relational data

log density for GBME models

Lazega's law firm data

Simulate an relational matrix based on a relative rank nomination scheme

Network embedding

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

Simulate a tobit relational matrix

Symmetric square root of a matrix

Edgelist to sociomatrix

Gibbs sampling of U and V

Gibbs sampling of U and V

Simulate Z given its expectation and covariance

Simulate Z given the partial ranks

Simulate Z given relative rank nomination data

Simulation from a multivariate normal distribution

Conditional simulation of additive effects and regression coefficients

Gibbs update for dyadic variance

rbeta_ab_rep_fc

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

Goodness of fit statistics

Griddy Gibbs update for dyadic correlation

Gibbs update for dyadic variance with independent replicate relational data

rank-based z-scores

Gibbs update for multiplicative effects covariance

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

Gibbs sampling of U and V

Simulation from a Wishart distribution

Simulate a and Sab from full conditional distributions under frn likelihood

Sampson's monastery data

International relations in the 90s

Censored binary nomination data and covariates

Linear combinations of submatrices of an array

binary relational data and covariates

AddHealth community 3 data

ordinal relational data and covariates

Fixed rank nomination data and covariates

binary relational data and covariates

row-specific ordinal relational data and covariates

normal relational data and covariates