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README.Rmd
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---
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%"
)
```
# fect
<!-- badges: start -->
[](https://www.tidyverse.org/lifecycle/#experimental)
[](https://opensource.org/licenses/MIT)
[<img src="https://cranlogs.r-pkg.org/badges/grand-total/gsynth" alt="downloads: CRAN"/>](https://cran.r-project.org/web/packages/gsynth/index.html)
[<img src="https://cranlogs.r-pkg.org/badges/grand-total/fect" alt="downloads: CRAN"/>](https://cran.r-project.org/web/packages/fect/index.html)
<!-- badges: end -->
**R** package for implementing counterfactual estimators, also known as imputation estimators, in panel fixed-effect settings. Suitable for causal panel analysis with binary treatments under (hypothetically) baseline randomization. It allows a treatment to switch on and off and limited carryover effects. It supports two-way fixed effects, linear factor models, and the matrix completion method.
Starting from v.2.0.0, all **gsynth** functionalities have been merged into **fect**.
**Github Repo:** [GitHub](https://github.com/xuyiqing/fect) (2.0.0)
**User Manual:** R code used in the [User Manual](https://yiqingxu.org/packages/fect/) can be downloaded from [here](fect_examples.R).
**Main References:**
Xu, Yiqing (2017). [Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models](https://www.cambridge.org/core/journals/political-analysis/article/generalized-synthetic-control-method-causal-inference-with-interactive-fixed-effects-models/B63A8BD7C239DD4141C67DA10CD0E4F3). *Political Analysis* 25 (1): 57--76.
Licheng Liu, Ye Wang, Yiqing Xu (2024). [A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data](https://yiqingxu.org/papers/english/2022_fect/LWX2022.pdf). *American Journal of Political Science*, 68 (1): 160--76.
## Installation
To install **fect** from CRAN, run the code chunk below:
```{r eval = FALSE, message = FALSE, warning = FALSE}
install.packages("fect")
```
We recommend users to install the most up-to-date version of **fect** from Github using:
```{r eval = FALSE, message = FALSE, warning = FALSE, cache = FALSE,}
devtools::install_github("xuyiqing/fect")
```
After installation, check **fect** version to make sure the package is up-to-date.
```{r}
installed.packages()["fect", "Version"]
```
**fect** depends on the following packages, which should be installed automatically when **fect** is being installed. You can also install them manually.
```{r eval=FALSE}
install_all <- function(packages) {
installed_pkgs <- installed.packages()[, "Package"]
for (pkg in packages) {
if (!pkg %in% installed_pkgs) {
install.packages(pkg)
}
}
}
packages <- c("abind", "doParallel", "doRNG", "fixest", "foreach", "future",
"GGally", "ggplot2", "grid", "gridExtra", "Mass",
"panelView", "Rcpp")
install_all(packages)
```
### Notes on installation failures
1. Intel Mac users may encounter compilation problems. See [here](http://yiqingxu.org/public/BigSurError.pdf) for a potential solution.
2. Windows users please consider upgrading R to 4.0.0 or higher and installing the [latest Rtools](https://cran.r-project.org/bin/windows/Rtools/) to avoid C++17 complier errors when installing fastplm.
3. For Rcpp, RcppArmadillo and MacOS "-lgfortran" and "-lquadmath" error, click [here]( http://thecoatlessprofessor.com/programming/rcpp-rcpparmadillo-and-os-x-mavericks-lgfortran-and-lquadmath-error/) for details.
4. Installation failure related to OpenMP on MacOS, click [here](http://thecoatlessprofessor.com/programming/openmp-in-r-on-os-x/) for a solution.
5. To fix these issues, try installing gfortran from [here]( https://gcc.gnu.org/wiki/GFortranBinaries#MacOS clang4 R Binaries from https://github.com/coatless/r-macos-clang).
## Report bugs
Please report any bugs to me (yiqingxu [at] stanford.edu) or submit an issue on [GitHub](https://github.com/xuyiqing/fect/issues). Please include your minimally replicable code & data file and a **panelView** treatment status plot. Your feedback is highly valued!