Changelog

capybara 2.0.0

  • Capybara now offers different variance-covariance estimators that do not require to call summary() (e.g., this differens from Alpaca). I added a vignette replicating Cameron and Miller (2014) to show how to use 1-way, 2-way, and dyadic clustering.

  • Besides vcov estimation, capybara now allows to update formulas, which is explained in the vcov vignette. tldr; you can use fml <- mpg ~ wt | am followed by felm(update(fml, . ~ . | cyl), data = mtcars and variations of (same for feglm() and cluster update)

  • I rewrote the the Rank-Revealing Cholesky general method to contribute back to Armadillo.

  • I found an error when using summary(lm/glm, type = "clustered") that largely underestimated the standard errors. This is now fixed and I merged “clustered” and ” sandwich” types into a single “sandwich” type for clarity and consistency as both use a bread-meat-bread approach.

  • The InferenceGLM struct now adds the VCOV matrix and the standard errors to reuse computation and
    strealine the summary creation. Same for the InferenceLM struct.

  • All the inference (std. error, R-squared, etc) was moved to C++ side and print() and summary() functions just format the output.

  • Allows formulas without fixed effects, such as y ~ x1 + x2 and y ~ x1 + x2 | 0 | cluster and formulas without slopes such as y ~ 0 | fe1 + fe2.

  • Allows offsets in GLMs.

  • All the computation is done on C++ side, and now model formulas explicitly fail if there are functions inside them.

  • The default is now predict(glm_object, type = "response"), unlike base R behavior.

  • Most of the R and C++ code was refactored to use memory efficiently.

  • Follows fixest-based normalization for fixed effects to match Stata results.

  • Provides the option to use control = list(centering = "berge") or list(centering = "stammann"). Both methods are equivalent but use different internal logics. Berge’s fixed point problem approach is usually faster.

  • Supports Probit and Logit regression.

  • Adds parallelization over columns for an efficient centering regardless of the method used.

  • A new function fepoisson_asymmetric() to compare coefficients across expectiles (e.g., 10%, 50%, 90%) to weight positive/negative residuals. This is based on “The Tails of Gravity” (10.1016/j.jinteco.2025.104145).

  • The summary_table() function now accepts positioning arguments for LaTeX. # capybara 1.8.1

  • Link to published article and citation info.

capybara 1.8.0

  • Drops congujate gradient acceleration and uses Irons-Tuck acceleration instead. It is slightly faster.
  • The benchmarks show a small overhead compared to fixest, which is much smaller memory footprint.

capybara 1.7.0

  • All the computation is done on C++ side. R does just do the data cleaning/wrangling.
  • Implements a rank-revealing Cholesky factorisation like fixest.
  • Returns estimated fixed effects by default (with an option not to).

capybara 1.6.0

  • Handles collinearities in the model matrix by using a QR decomposition. when Cholesky fails.
  • It can return NA coefficients when there is collinearity to match base R outputs.

capybara 1.4.0

  • Adds an extended battery of optional tests for the Poisson model.
  • Modular code for easier maintenance.

capybara 1.3.0

  • Explicitly avoids Intel MKL and fallbacks to OpenBLAS to avoid issues with non reproducible results.
  • Uses OpenMP to parallelize the demeaning functions, which can lead to significant speedups in large datasets.
  • Uses Irons-Tuck acceleration for fast convergence in the demeaning functions.

capybara 1.2.0

  • Changes to fit and summary functions to report perfectly classified observations.
  • Dropped linear dependence checks, leaving it to the Cholesky decomposition to handle it.

capybara 1.1.0

  • The workhorse demeaning functions were rewritten towards a more efficient implementation. This is based on ppmlhdfe and fixest code.
  • Loops were avoided and replace with efficient matrix operations.

capybara 1.0.3

  • Implements some ideas from reghdfe/ppmlhdfe to improve the centering/demeaning functions.

capybara 1.0.2

  • Small refactors for speed.

capybara 1.0.1

  • The examples now use smaller datasets to avoid CRAN timeouts with Clang-ASAN.

capybara 1.0.0

  • Implements a new approach to obtain the rank with a QR decomposition without loss of stability.
  • Adds different refactors to:
    • Streamline the code
    • Pass all large objects by reference
    • Use BLAS/LAPACK instead of iteration for some operations
  • Uses a new configure file that works nicely with Intel MKL (i.e. the user does not need to export environment variables for the package to detect MKL).

capybara 0.9.6

  • Calculates the rank of matrix X based on singular value decomposition instead of QR decomposition. This is more efficient and numerically stable.

capybara 0.9.5

  • Fixes and expands the ‘weights’ argument in the fe*() functions to allow for different types of weights. The default is still NULL (i.e., all weights equal to 1). The argument now admits weights passed as weights = ~cyl, weights = mtcars$cyl, or w <- mtcars$cyl; weights = w.

capybara 0.9.4

  • Allows to estimate models without fixed effects.

capybara 0.9.3

  • Fixes the tidy() method for linear models (felm class). Now it does not require to load the tibble package to work.
  • Adds a wrapper to present multiple models into a single table with the option to export to LaTeX.

capybara 0.9.2

  • Implements Irons and Tuck acceleration for fast convergence.

capybara 0.9.1

  • Fixes a minor uninitialized variable in the C++ code used for a conditional check.

capybara 0.9

  • First CRAN version

  • Refactored functions to avoid data copies:

    • center variables
    • crossprod
    • GLM and LM fit
    • get alpha
    • group sums
    • mu eta
    • variance

  • iter_center_max and iter_inner_max now can be modified in feglm_control().

capybara 0.8.0

  • Dedicated functions for linear models to avoid the overhead of running the GLM function with a Gaussian link.

capybara 0.7.0

  • The predict method now allows to pass new data to predict the outcome.
  • Fully documented code and tests according to rOpenSci standards.

capybara 0.6.0

  • Moves all the heavy computation to C++ using Armadillo and it exports the results to R. Previously, there were multiple data copies between R and C++ that added overhead to the computations.
  • The previous versions returned MX by default, now it has to be specified.
  • Adds code to extract the fixed effects with felm objects.

capybara 0.5.2

  • Uses an O(n log(n)) algorithm to compute the Kendall correlation for the pseudo-R2 in the Poisson model.

capybara 0.5.1

  • Using arma::field consistently instead of std::vector<std::vector<>> for indices.
  • Linear algebra changes, such as using arma::inv instead of solving arma::qr for the inverse.
  • Replaces multiple for loops with dedicated Armadillo functions.

capybara 0.5.0

  • Avoids for loops in the C++ code, and instead uses Armadillo’s functions.
  • O(n) computations in C++ access data directly by using pointers.

capybara 0.4.6

  • Fixes notes from tidyselect regarding the use of all_of().
  • The C++ code follows a more consistent style.
  • The GH-Actions do not test gcc 4.8 anymore.

capybara 0.4.5

  • Ungroups the data to avoid issues with the model matrix

capybara 0.4

  • Uses R’s C API efficiently to add a bit more of memory optimizations

capybara 0.3.5

  • Uses Mat consistently for all matrix operations (avoids vectors)

capybara 0.3

  • Reduces memory footprint ~45% by moving some computation to Armadillo’s side

capybara 0.2

  • Includes pseudo R2 (same as Stata) for Poisson models

capybara 0.1

  • Initial CRAN submission.

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