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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: 5 days 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 (9 months ago)
- Default Branch: main
- Last Pushed: 2024-09-20T00:21:35.000Z (14 days ago)
- Last Synced: 2024-09-25T20:57:22.307Z (8 days ago)
- Topics: cpp11, econometrics, linear-models, rstats
- Language: R
- Homepage: http://pacha.dev/capybara/
- Size: 1.84 MB
- Stars: 13
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
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://codecov.io/gh/pachadotdev/capybara)
[](https://www.buymeacoffee.com/pacha)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
## About
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. 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 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) and Stammann (2018) works on
OLS and GLM estimation with large fixed effects implemented in the 'lfe' and
'alpaca' packages. The differences are that 'capybara' does not use C nor Rcpp
code, instead it uses cpp11 and
[cpp11armadillo](https://github.com/pachadotdev/cpp11armadillo).
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
remotes::install_github("pachadotdev/capybara")
```
## Examples
See the documentation in progress: https://pacha.dev/capybara.
## Design choices
Capybara uses C++ and vectorized R operations to address bottlenecks where
possible. Some parts of the code use 'dplyr', which allows me to write code that
is easier to understand and it works well to performed grouped operations. The
intensive computations are done on C++ side. I tried to implement this idea from
v0.2 and onwards: "He who gives up code safety for code speed deserves
neither." (Wickham, 2014).
I know some parts of the code are not particularly easy to understand. For
example, such as my implementation of Kendall's Tau (or Kendall's
correlation) with a time complexity of O(n * log(n)) instead of O(n^2). I still
did my best to write a straightforward code.
Capybara is full of trade-offs. I have a branch where I used 'dplyr' and
'dtplyr' to help myself with the 'data.table' syntax, otherwise there is no way
to use in-place modification of data. Because 'data.table' modifies the original
data (e.g., it converts 'data.frame' and 'tibble' structures into 'data.table'
structures), the main branch uses 'dplyr' to avoid side effects.
In my research I use 'SQL' because I have over 200 GB of international trade
data, where 'dplyr' helps a lot because it allows me to query 'SQL' directly
from R mand just using 'dplyr' syntax, something impossible with 'data.table',
which requires me to go to the 'SQL' editor en export my queries in CSV format
and then import them in R. The downside is that 'dplyr' is slower than
'data.table' and uses more memory.
I think with my design choices I accomplished my goal of fitting models in my
laptop instead of relying on UofT's servers.
## Future plans
I will add a RESET test.
There are a few tests but these have to be expanded.
## Benchmarks
Median time for the different models in the book
[An Advanced Guide to Trade Policy Analysis](https://www.wto.org/english/res_e/publications_e/advancedguide2016_e.htm).
| package | PPML | Trade Diversion | Endogeneity | Reverse Causality | Non-linear/Phasing Effects | Globalization |
|:------------|-------:|-----------------:|------------:|-----------------:|----------------------------:|--------------:|
| Alpaca | 0.4s | 2.6s | 1.6s | 2.0s | 3.1s | 5.3s |
| Base R | 120.0s | 2.0m | 1380.0s | 1440.0s | 1380.0s | 1500.0s |
| **Capybara**| 0.3s | 2.0s | 1.2s | 1.4s | 1.7s | 3.4s |
| Fixest | 0.1s | 0.5s | 0.1s | 0.2s | 0.3s | 0.5s |
Memory allocation for the same models
| package | PPML | Trade Diversion | Endogeneity | Reverse Causality | Non-linear/Phasing Effects | Globalization |
|:-------------|--------:|----------------:|------------:|------------------:|---------------------------:|--------------:|
| Alpaca | 307MB | 341MB | 306MB | 336MB | 395MB | 541MB |
| Base R | 3000MB | 3000MB | 12000MB | 12000GB | 12000GB | 12000MB |
| **Capybara** | 27MB | 32MB | 20MB | 23MB | 29MB | 43MB |
| Fixest | 44MB | 36MB | 27MB | 32MB | 41MB | 63MB |
## Changing the number of cores
Note that you can edit the `Makevars` file to change the number of cores that
capybara uses, here is an example of how it affects the performance
| cores | PPML | Trade Diversion | Endogeneity | Reverse Causality | Non-linear/Phasing Effects | Globalization |
|:------|-------:|-----------------:|------------:|------------------:|---------------------------:|--------------:|
| 2 | 1.8s | 16.2s | 7.7s | 9.6s | 13.0s | 24.0s |
| 4 | 1.7s | 16.0s | 7.4s | 9.3s | 12.3s | 23.6s |
| 6 | 0.7s | 2.4s | 2.0s | 2.0s | 2.5s | 4.0s |
| 8 | 0.3s | 2.0s | 1.2s | 1.4s | 1.7s | 3.4s |
## Testing and debugging
## Testing
I use `testthat` (e.g., `devtools::test()`) to compare the results with base R.
These tests are about the correctness of the results.
### Debuging
I run `r_valgrind "dev/valgrind-kendall-correlation.r"` or the corresponding
test from the project's root in a new terminal (bash) after running
`devtools::install()`. These tests are about memory leaks (e.g., I use
repeteated computations and sometimes things such as "pi = 3").
This works because I previously defined this in `.bashrc`, to make it work you
need to run `source ~/.bashrc` or reboot your computer.
```
function r_debug_symbols () {
# if src/Makevars does not exist, exit
if [ ! -f src/Makevars ]; then
echo "File src/Makevars does not exist"
return 1
fi
# if src/Makevars contains a line that says "PKG_CPPFLAGS"
# but there is no "-UDEBUG -g" on it
# then add "PKG_CPPFLAGS += -UDEBUG -g" at the end
if grep -q "PKG_CPPFLAGS" src/Makevars; then
if ! grep -q "PKG_CPPFLAGS.*-UDEBUG.*-g" src/Makevars; then
echo "PKG_CPPFLAGS += -UDEBUG -g" >> src/Makevars
fi
fi
# if src/Makevars does not contain a line that reads
# PKG_CPPFLAGS ...something... -UDEBUG -g ...something...
# then add PKG_CPPFLAGS = -UDEBUG -g to it
if ! grep -q "PKG_CPPFLAGS.*-UDEBUG.*-g" src/Makevars; then
echo "PKG_CPPFLAGS = -UDEBUG -g" >> src/Makevars
fi
}
function r_valgrind () {
# if no argument is provided, ask for a file
if [ -z "$1" ]; then
read -p "Enter the script to debug: " script
else
script=$1
fi
# if no output file is provided, use the same filename but ended in txt
if [ -z "$2" ]; then
output="${script%.*}.txt"
else
output=$2
fi
# if the file does not exist, exit
if [ ! -f "$script" ]; then
echo "File $script does not exist"
return 1
fi
# if the file does not end in .R/.r, exit
shopt -s nocasematch
if [[ "$script" != *.R ]]; then
echo "File $script does not end in .R or .r"
return 1
fi
shopt -u nocasematch
# run R in debug mode, but after that we compiled with debug symbols
# see https://reside-ic.github.io/blog/debugging-memory-errors-with-valgrind-and-gdb/
# R -d 'valgrind -s --leak-check=full --show-leak-kinds=all --track-origins=yes' -f $script 2>&1 | tee valgrind.txt
R --vanilla -d 'valgrind -s --track-origins=yes' -f $script 2>&1 | tee $output
}
# create an alias for R
alias r="R"
alias rvalgrind="R --vanilla -d 'valgrind -s --track-origins=yes'"
```
`r_debug_symbols` makes everything slower, but makes sure that all compiler
optimizations are disabled and then valgrind will point us to the lines that
create memory leaks.
`r_valgrind` will run an R script and use Linux system tools to test for
initialized values and all kinds of problems that result in memory leaks.
When you are ready testing, you need to remove `-UDEBUG` from `src/Makevars`.
## 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.