R package for probit regression with fixed effects for networks

While preparing my paper for network probit regression with fixed effects for publication, I have finally gotten around to publishing the R package that implements all the methods that I propose in the paper:

Andreas Dzemski: “An empirical model of dyadic link formation in a network with unobserved heterogeneity”, Review of Econnomics and Statistics, forthcoming.

The package makes it very easy to apply my methods to any network data. The package repository is hosted on Github and can be downloaded and installed directly from within R using the devtools package. Please follow the installation instructions in the README file on Github.

The README file also provides a detailed step-by-step guide for how to use the package to analyze network data. Even if you have never worked with R before, this guide should make it fairly easy to use the package in your research.

I don’t have any plans for implementing the methods in Stata. If you are a researcher who is working primarily in Stata, note that R integrates very well with Stata. To load a Stata dataset in R make sure that the readstata13 package is installed by typing

  install.packages("readstata13")

Now, load your Stata dataset “my_dir/network_data.dta” by typing

  dyadic_data <- read.dta13("my_dir/network_data.dta")

If you are researcher working with Stata and if you have trouble running your analysis in R, please let me know and I will try to assist.

To make sure that the procedures in the package run fairly fast, I have implemented all the computational bottlenecks in fairly efficient C++. The R ecosystem makes it very easy to combine R and C++ code in a platform agnostic way. As far as I know, this is impossible to do for Stata.