Changes in version 1.3.0                        

This release substantially speeds up the 2x2 estimators and tightens the
handling of ill-conditioned designs. Point estimates, standard errors,
and influence functions are unchanged up to floating-point precision
(~1e-14).

Performance

  - The propensity-score and outcome regressions now use fastglm's
    low-level entry point (fastglmPure) in place of the fastglm()
    wrapper, skipping the per-call input-coercion and family/deviance
    bookkeeping that was paid on every fit. This is applied throughout:
    the point estimators, every weighted-bootstrap helper (which refits
    on each of nboot iterations), and the IPT/calibration propensity
    initializer.
    
      - The influence-function computations use crossprod() and BLAS dot
        products in place of colMeans(. * int.cov) and t(int.cov) %*% (W
        * int.cov).
    
      - Together these make the 2x2 estimators roughly 1.5x faster, with
        the weighted bootstrap benefiting proportionally.

Robustness

  - The propensity-score Hessian is now checked for singularity with
    rcond() before inversion, matching the long-standing check on the
    outcome-regression design. A near-singular propensity-score design
    previously yielded a silently incorrect standard error; it now stops
    with an informative message.
    
      - std_ipw_did_rc now warns when the propensity-score estimation
        does not converge and stops when the estimated coefficients are
        NA, matching the other propensity-score estimators (these guards
        were previously missing, so a rank-deficient design returned a
        corrupted standard error silently).
    
      - The repeated cross-section pre-processing now warns when
        collinear covariates are dropped, matching the panel
        pre-processing (previously the repeated cross-section path
        dropped them without notice).

Internal

  - Added a numeric regression-lock test suite that pins the ATT and
    standard error of every exported estimator, asserts the
    influence-function invariants, and checks that singular designs and
    negative weights are rejected.

                 Changes in version 1.2.3 (2025-12-04)                  

  - Fix typo on non-stabilized IPW with trimming
      - Unify degree of freedom adjustments in analytical std errors

                 Changes in version 1.2.2 (2025-05-30)                  

  - Add trimming argument to avoid severe overlap problems. The default
    is to trim the propensity score in the comparison group that is
    above 0.995.

                        Changes in version 1.2.1                        

  - Fix typo on returning influence functions for TWFE regressions.

                 Changes in version 1.2.0 (2024-10-07)                  

  - Improve code to avoid redundant data checks
    
      - Use fastglm instead of parglm for improved speed

                 Changes in version 1.1.0 (2024-09-05)                  

  - Restore solve as default to invert matrix, as it is faster than
    qr.solve for small matrices.
    
      - Improve error handling for non-invertible matrices.
    
      - Changing estimation methods for fastglm and parglm (in place of
        lm and glm).
    
      - Do not let the estimated propensity score be above 1 - 1e-6
        (instead of 1 - e-16).

                 Changes in version 1.0.7 (2024-07-05)                  

  - Speed up data processing using Rcpp
    
      - The weights are now enforced to be normalized and have mean 1
        across all observations.

                 Changes in version 1.0.6 (2023-05-18)                  

  - Use qr.solve as default (instead of solve)
    
      - Drop collinear variables in pre_process_drdid.R (useful in drdid
        command but not other commands)
    
      - Add compatibility with R 3.5
    
      - Improve invertibility of outcome regression design matrix

                 Changes in version 1.0.4 (2022-07-19)                  

  - Fixed links

                 Changes in version 1.0.3 (2021-11-13)                  

  - Add new flags for non-unique unit identifier
    
      - Better handle of factor variables as covariates

                        Changes in version 1.0.2                        

  - Fix issue with NA in covariates

                 Changes in version 1.0.1 (2021-01-07)                  

  - Allows for treating covariates as factor and alike when computing
    DiD
    
      - Improve error and warning handling due to collinearity and
        convergence issues.

                 Changes in version 1.0.0 (2020-05-18)                  

  - First official version of package, functions for computing a variety
    of difference-in-differences (DiD) estimators for the ATT.
    
      - Documentation is improved compared to the devel version,
        including examples for every function now.
    
      - Created wrapper function drdid, ordid and ipwdid to implement
        doubly-robust, outcome regression and inverse probability
        weighted DID estimators.
    
      - Add dataset used in the empirical application of Sant'Anna and
        Zhao (2020).