PartialNetwork-package: The PartialNetwork package

PartialNetwork-packageR Documentation

The PartialNetwork package


The PartialNetwork package implements instrumental variables (IV) and Bayesian estimators for the linear-in-mean SAR model (e.g. Bramoulle et al., 2009) when the distribution of the network is available, but not the network itself. To make the computations faster PartialNetwork uses C++ through the Rcpp package (Eddelbuettel et al., 2011).


Two main functions are provided to estimate the linear-in-mean SAR model using only the distribution of the network. The function sim.IV generates valid instruments using the distribution of the network (see Propositions 1 and 2 in Boucher and Houndetoungan (2020)). Once the instruments are constructed, one can estimate the model using standard IV estimators. We recommend the function ivreg from the package AER (Kleiber et al., 2020). The function mcmcSAR performs a Bayesian estimation based on an adaptive MCMC (Atchade and Rosenthal, 2005). In that case, the distribution of the network acts as prior distribution for the network.
The package PartialNetwork also implements a network formation model based on Aggregate Relational Data (McCormick and Zheng, 2015; Breza et al., 2017). This part of the package relies on the functions rvMF, dvMF and logCpvMF partly implemented in C++, but using code from movMF (Hornik and Grun, 2014).


Maintainer: Elysee Aristide Houndetoungan



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See Also

Useful links:

ahoundetoungan/PartialNetwork documentation built on Feb. 8, 2023, 9:27 p.m.