# 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.