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NEWS

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

• 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).

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

• 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.

• Allows to estimate models without fixed effects.

• 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.

• Implements Irons and Tuck acceleration for fast convergence.

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

• 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().

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

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

• 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.

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

• 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.

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

• 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.

• Ungroups the data to avoid issues with the model matrix

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

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

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

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

• Initial CRAN submission.

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