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rwig

DOI: 10.1016/j.econlet.2019.108874 experimental version R build status License: MIT

The rwig package implements the Sinkhorn algorithms for regularized Optimal Transport problems, Wasserstein Barycenter algorithms for the regularized Wasserstein Barycenter problems, Wasserstein Dictionary Learning (WDL) model, and Wasserstein Index Generation (WIG) model in R (see references below).

All the methods are implemented from the ground up with C++ and Armadillo (with Rcpp and RcppArmadillo), with additional support for multi-threading for the log-stablized methods for sinkhorn and barycenter. See the vignette on multi-threading for faster processing.

Installation

The package is currently under heavy development and can only be considered as alpha stage. You can install the development version of rwig from GitHub with:

# install.packages("pak")
pak::pak("fangzhou-xie/rwig")

Get Started

Please check out all the vignettes for the examples of using this package under the “Articles” drop down menu on the documentation website.

Citation

Please use the following to cite my works:

@article{xie2020,
  title = {Wasserstein Index Generation Model: Automatic Generation of Time-Series Index with Application to Economic Policy Uncertainty},
  author = {Xie, Fangzhou},
  year = 2020,
  month = jan,
  journal = {Economics Letters},
  volume = {186},
  pages = {108874},
  issn = {0165-1765},
  doi = {10.1016/j.econlet.2019.108874},
  urldate = {2019-12-10},
}

Reference

Peyré, G., & Cuturi, M. (2019). Computational Optimal Transport: With Applications to Data Science. Foundations and Trends® in Machine Learning, 11(5–6), 355–607. https://doi.org/10.1561/2200000073

Schmitz, M. A., Heitz, M., Bonneel, N., Ngolè, F., Coeurjolly, D., Cuturi, M., Peyré, G., & Starck, J.-L. (2018). Wasserstein dictionary learning: Optimal transport-based unsupervised nonlinear dictionary learning. SIAM Journal on Imaging Sciences, 11(1), 643–678. https://doi.org/10.1137/17M1140431

Xie, F. (2020). Wasserstein index generation model: Automatic generation of time-series index with applieion to economic policy uncertainty. Economics Letters, 186, 108874. https://doi.org/10.1016/j.econlet.2019.108874

Xie, F. (2025). Deriving the Gradients of Some Popular Optimal Transport Algorithms (No. arXiv:2504.08722). arXiv. https://doi.org/10.48550/arXiv.2504.08722

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.