yoojeonglee/polnet

R package for estimating political networks

polnet: A Statistical Analysis of Political Networks

This R package provides a computationally efficient way of fitting the Latent Space Network Model (LSNM) and bipartite Link Community Model (biLCM) developed by Kim and Kunisky (2018).

Authors

In Song Kim, [Dmitry Kunisky], Jacob Jaffe

Paper

Mapping Political Communities: A Statistical Analysis of Lobbying Networks in Legislative Politics

Installation

There is currently no package available for the LNSM or biLCM models on CRAN. It is possible to download the package through GitHub instead.

First, make sure the devtools package is installed.

if(!require(devtools)) install.packages("devtools")

This only has to be done if devtools is not already installed and only has to be done once.

Then, use the install_github() function from devtools to install the package.

library(devtools)
install_github("insongkim/polnet",dependencies=TRUE)

Example

This example uses randomly generated poisson data:

#Load polnet package
datmat = matrix(rpois(500, 1), ncol = 10)
lnsmR = LSNM(edges = datmat, D = 2)

• edges is the matrix of connection strength data, a matrix or an object coercible to a matrix by as.matrix()
• D is the dimensionality of the latent space model, defaults to 2

One can also use the built-in functions random_LSNM_data and random_biLCM_data to generate data based off of known parameters. For example,

# Load polnet package
comData = random_biLCM_data(m = 10, n = 7, k = 4)
biLCMR = biLCM(A = comData[A_mat], m = 10, n = 7, k = 4)

• m is the number of clients
• n is the number of politicians
• k is the number of link communities
• A is the matrix of connection strength data

Test data, describing the House of Representative voting records of the 115th Congress, is also included in this package. With politicians and bills making up the two different groups, this dataset can be subject to LSNM and biLCM analysis.