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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.