README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# rwig

```{r, echo = FALSE, results = "hide", message = FALSE}
# use badges
library(badger)
```

<!-- badges: start -->

```{r, echo = FALSE, results = "asis"}
cat(
  badge_doi("10.1016/j.econlet.2019.108874", "yellow",
            "DOI: 10.1016/j.econlet.2019.108874"),
  badge_lifecycle("experimental", alt = "experimental version"),
  badge_cran_release("rwig", "green"),
  badge_cran_download("rwig", "grand-total", "green"),
  badge_cran_checks("rwig"),
  badge_github_actions("fangzhou-xie/rwig", "R-CMD-check.yaml"),
  badge_license()
)
```

<!-- badges: end -->

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 **W**asserstein **I**ndex **G**eneration (**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](https://fangzhou-xie.github.io/rwig/reference/sinkhorn.html) and
[barycenter](https://fangzhou-xie.github.io/rwig/reference/barycenter.html).
See the [vignette](https://fangzhou-xie.github.io/rwig/articles/threading.html)
on multi-threading for faster processing.

## Installation

This package is on [CRAN](https://cran.rstudio.com/), and I recommend
to use the `pak` to install it:

```r
# install pak if not already done so
# install.packages("pak")
pak::pak("rwig")

# or you can install it in the classic way
install.packages("rwig")
```

<!-- 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](https://github.com/) with:

```r
# 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](https://fangzhou-xie.github.io/rwig/).

## 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