Fangzhou Xie

Photo courtesy of Roni Aviv.

Hi, my name is Fangzhou Xie. I am currently a Ph.D. Candidate at the Department of Economics at Rutgers University. Previously, I got my M.A. at the Department of Economics at New York University.

Starting from 2026, I am serving as Associate Editors (AE) for the journals Software Impacts and Journal of Open Source Software.

I am a fan of R/Rcpp/RcppEigen/RcppArmadillo/RcppThread, and I am also a developer of several R packages. You can check out my repositories on Github. Most notably, I am the creator and maintainer of the widely-used rethnicity package.

Email: [FIRSTNAME][dot][LASTNAME][at]rutgers[dot]edu

news

Feb 13, 2026	I joined the Editorial Boards for Software Impacts and Journal of Open Source Software.
Mar 14, 2025	My package `rethnicity v0.2.7` is now on CRAN.
Oct 18, 2024	My package `rethnicity v0.2.6` is now on CRAN.

latest posts

Oct 03, 2024	Another Upgrade of My Website

selected publications

Wasserstein Index Generation Model: Automatic Generation of Time-Series Index with Application to Economic Policy Uncertainty

Fangzhou Xie

Economics Letters, Jan 2020

Abs

I propose a novel method, the Wasserstein Index Generation model (WIG), to generate a public sentiment index automatically. To test the model’s effectiveness, an application to generate Economic Policy Uncertainty (EPU) index is showcased.
Rethnicity: An R Package for Predicting Ethnicity from Names

Fangzhou Xie

SoftwareX, Jan 2022

Abs

In this study, a new R package, rethnicity 11https://github.com/fangzhou-xie/rethnicity. It has also been published on [CRAN]. is provided for predicting ethnicity based on names. The Bidirectional Long Short-Term Memory (Bi-LSTM), a recurrent neural network architecture commonly used for natural language processing, was chosen as the model for our study. The Florida Voter Registration was used as the training and testing data. Special care was given for the accuracy of minority groups by adjusting the imbalance in the dataset. The models were trained and exported to C++ and then integrated with R using Rcpp. Additionally, the availability, accuracy, and performance of the package were compared with other solutions.