Package: capybara 2.1.0

Mauricio Vargas Sepulveda

capybara: Fast and Memory Efficient Fitting of Linear Models with High-Dimensional Fixed Effects

Fast and user-friendly estimation of generalized linear models with multiple fixed effects and cluster the standard errors. The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) <doi:10.48550/arXiv.1707.01815>, Gaure (2013) <doi:10.1016/j.csda.2013.03.024>, Berge (2018) <https://ideas.repec.org/p/luc/wpaper/18-13.html>, and Correia et al. (2020) <doi:10.1177/1536867X20909691>. This implementation is described in Vargas Sepulveda (2025) <doi:10.1371/journal.pone.0331178>.

Authors:Mauricio Vargas Sepulveda [aut, cre], Joao Santos Silva [ths], Julian Costas Fernandez [ctb], Yoto Yotov [ctb]

capybara_2.1.0.tar.gz
capybara_2.1.0.zip(r-4.7-x86_64)capybara_2.1.0.zip(r-4.7-arm64)capybara_2.1.0.zip(r-4.6-x86_64)capybara_2.1.0.zip(r-4.6-arm64)
capybara_2.1.0.tar.gz(r-4.7-arm64)capybara_2.1.0.tar.gz(r-4.7-x86_64)capybara_2.1.0.tar.gz(r-4.6-arm64)capybara_2.1.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
capybara/json (API)

# Install 'capybara' in R:
install.packages('capybara', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pachadotdev/capybara/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

openblascppopenmp

3.60 score 474 downloads 13 exports 22 dependencies

Last updated from:11d43fee9d. Checks:9 OK, 1 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK136
linux-devel-x86_64OK176
source / vignettesOK264
linux-release-arm64OK128
linux-release-x86_64OK167
windows-devel-arm64OK222
windows-devel-x86_64OK162
windows-release-arm64OK267
windows-release-x86_64OK156
wasm-releaseFAIL110

Exports:augmentautoplotfe_tablefeglmfelmfenegbinfepoissonfepoisson_asymmetricfit_controlglancesandwich_vcovsummary_tabletidy

Dependencies:armadillo4rclicpp11cpp4rfarverFormulagenericsggplot2gluegtableisobandlabelinglifecycleMASSR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Asymmetric Poisson Pseudo-Maximum Likelihood (APPML)
Overview | Estimating lower and upper expectiles | Comparing coefficients across expectiles | Convergence diagnostics | References
Rendered fromasymmetric.Rmdusingknitr::rmarkdown

Last update: 2026-06-29
Started: 2026-06-15

Different Variance-Covariance Estimators
IID (no cluster part in formula) | Heteroskedastic-robust (HC0) | One-way: cluster on country-pair (g,h) | One-way: cluster on country 1 (g) | One-way: cluster on country 2 (h) | Two-way: cluster on (g) and (h) simultaneously | Dyadic-robust: Cameron-Miller (2014) sandwich with cross-dyad correlations
Rendered fromvariance-covariance.Rmdusingknitr::rmarkdown

Last update: 2026-06-15
Started: 2026-06-15

Nonexistence of estimates of Poisson models
Example 1 | Example 2 | Example 3 (fixed effects)
Rendered fromseparation.Rmdusingknitr::rmarkdown

Last update: 2026-06-15
Started: 2026-06-15

Poisson Pseudo-Maximum Likelihood (PPML) Model with Cluster-Robust Standard Errors
References
Rendered fromintro.Rmdusingknitr::rmarkdown

Last update: 2026-06-15
Started: 2025-03-24