https://github.com/pachadotdev/capybara
tldr; If you have a 2-4GB dataset and you need to estimate a (generalized) linear model with a large number of fixed effects, this package is for you.
https://github.com/pachadotdev/capybara
cpp11 econometrics linear-models rstats
Last synced: about 2 months ago
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tldr; If you have a 2-4GB dataset and you need to estimate a (generalized) linear model with a large number of fixed effects, this package is for you.
- Host: GitHub
- URL: https://github.com/pachadotdev/capybara
- Owner: pachadotdev
- License: apache-2.0
- Created: 2024-01-07T22:25:44.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2026-02-24T01:46:30.000Z (2 months ago)
- Last Synced: 2026-02-24T03:13:34.873Z (2 months ago)
- Topics: cpp11, econometrics, linear-models, rstats
- Language: R
- Homepage: http://pacha.dev/capybara/
- Size: 4.38 MB
- Stars: 22
- Watchers: 1
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
- Codemeta: codemeta.json
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# capybara 
[](https://github.com/pachadotdev/capybara/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/pachadotdev/capybara)
[](https://buymeacoffee.com/pacha)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://CRAN.R-project.org/package=capybara)
## About
Please read my article for the full details about capybara 1.x.y (Open Access):
> Vargas Sepulveda, Mauricio. 2025. 'Capybara: Efficient Estimation of Generalized Linear Models with High-Dimensional Fixed Effects'. *PLOS ONE* 20 (9): e0331178. https://doi.org/10.1371/journal.pone.0331178.
I am preparing the manuscript for capybara 2.x.y that is about speed and memory improvements.
If you have a 2-4GB dataset and you need to estimate a (generalized)
linear model with a large number of fixed effects, this package is for you. It
works with larger datasets as well and facilites computing clustered standard
errors.
'capybara' is a fast and small footprint software that provides efficient
functions for demeaning variables before conducting a GLM estimation. This
technique is particularly useful when estimating linear models with multiple
group fixed effects. It is a fork of the excellent Alpaca package created and
maintained by [Dr. Amrei Stammann](https://github.com/amrei-stammann). The
software can estimate Exponential Family models (e.g., Poisson) and Negative
Binomial models.
Traditional QR estimation on the full design matrix can be unfeasible due to
additional memory requirements. The method, which is based on Halperin (1962)
vector projections offers important time and memory savings without compromising
numerical stability in the estimation process.
The software heavily borrows from Gaure (2013), Stammann (2018) and Berge (2018)
works on OLS and GLM estimation with large fixed effects implemented in the 'lfe',
'alpaca' and 'fixest' packages. The differences are that 'capybara' does not use C
nor Rcpp code, instead it uses cpp4r and
[armadillo4r](https://github.com/pachadotdev/armadillo4r).
The summary tables borrow from Stata outputs. I have also provided integrations
with 'broom' to facilitate the inclusion of statistical tables in Quarto/Jupyter
notebooks.
If this software is useful to you, please consider donating on
[Buy Me A Coffee](https://buymeacoffee.com/pacha). All donations will
be used to continue improving `capybara`.
## Installation
You can install the development version of capybara like so:
```r
install.packages("capybara", repos = "https://cloud.r-project.org/")
```
You can install the development version of capybara like so:
```r
remotes::install_github("pachadotdev/capybara")
```
## Examples
See the documentation: https://pacha.dev/capybara/.
Here is simple example of estimating a linear model and a Poisson model with fixed effects:
```r
m1 <- felm(mpg ~ wt | cyl, mtcars)
m2 <- fepoisson(mpg ~ wt | cyl, mtcars)
summary_table(m1, m2, model_names = c("Linear", "Poisson"))
| Variable | Linear | Poisson |
|------------------|---------------------|-------------------|
| wt | -3.206*** | -0.180* |
| | (0.295) | (0.072) |
| | | |
| Fixed effects | | |
| cyl | Yes | Yes |
| | | |
| N | 32 | 32 |
| R-squared | 0.837 | 0.616 |
Standard errors in parenthesis
Significance levels: *** p < 0.001; ** p < 0.01; * p < 0.05; . p < 0.1
```
## Installing with compiler optimizations
CRAN packages are built with the `-O2` compiler flag, which is
sufficient for most packages, including capybara. However, if you want to use
the maximum compiler optimizations, you can do so by setting the `-O3`
compiler flag.
To do that, create a user Makevars file in your home directory
(`~/.R/Makevars`) and add the following lines:
```makefile
# Copy to ~/.R/Makevars if you want to override R's default optimization
CXXFLAGS = -O3
CXX11FLAGS = -O3
CXX14FLAGS = -O3
CXX17FLAGS = -O3
CXX20FLAGS = -O3
```
Additional optimizations can be enabled by setting the `CAPYBARA_OPTIMIZATIONS`
environment variable to "yes" and choosing the number of cores to use for parallel
processing (the default is to use 50% of the available cores). You can do this in your
R session like so:
```r
# Install local version
Sys.setenv(CAPYBARA_OPTIMIZATIONS = "yes")
Sys.setenv(CAPYBARA_CORES = 4) # Set the number of cores to use (optional)
install.packages(".", repos = NULL, type = "source")
# or
devtools::install()
```
This will determine if your hardware allows hardware-specific compiler flags
that provide significant performance improvements (sometimes 2-4x faster than
just using portable flags).
## Code of Conduct
Please note that the capybara project is released with a
[Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.
## Acknowledgements
Thanks a lot to [Prof. Yoto Yotov](https://yotoyotov.com/) for reviewing the summary functions.