Changes in version 0.2.0                        

New Features

  - Clarke test (clarketest()): Full implementation of Clarke's (2007)
    distribution-free test for non-nested models. Includes both
    binomial/sign and signed-rank (Wilcoxon) variants, three penalty
    options (none, akaike, schwarz), model-based bootstrap, and an
    informative print method with expected values under the null and
    robustness notes.

  - Vuong test (vuongtest()): Now supports the same three penalty
    options (none, akaike, schwarz) for consistency with Clarke's test
    and Vuong (1989). Bootstrap functionality has been added and aligned
    with the rest of the test suite.

  - Wald test (waldtest()): Major upgrade — new constraints argument
    (replaces coef_names) that supports arbitrary linear combinations of
    coefficients.

Improvements

  - Much better handling of weights across all tests and estimators.
    Statistics are now properly scaled to ensure correct asymptotic
    behavior.
  - Enhanced logLik(), AIC(), and BIC() methods to return effective or
    scaled statistics depending on user request.
  - Improved interaction with maxLik optimizer, leading to more reliable
    convergence in many models.
  - Enhanced print methods for hypothesis tests (clearer conclusions,
    reference values under the null, bootstrap robustness warnings).
  - Better print methods for summaries of weighted models, showing both
    effective and scaled (to sample size) measures where appropriate.
  - Updated documentation and examples for non-nested model comparison.

Bug fixes & minor changes

  - Fixed bootstrap storage (numeric vectors instead of character).
  - Various internal cleanups and consistency improvements across the
    test suite.

                 Changes in version 0.1.2 (2026-05-08)                  

  - Added return values in the documentation of exported functions that
    were missing them.
  - Added references to implemented methods in the description.

                        Changes in version 0.1.1                        

  - Fixed weighted log-likelihood calculation in ml_logit() (both
    homoskedastic and heteroskedastic versions). This bug previously
    caused incorrect log-likelihood, AIC, BIC, and convergence issues in
    weighted logit models.
  - All other models were already handling weights correctly.

                        Changes in version 0.1.0                        

  - First public release - Initial CRAN submission
  - Provides maximum likelihood estimation for Gaussian (linear and
    log-normal), logit, probit, Poisson, negative binomial (NB1 and
    NB2), gamma, and beta models.
  - Consistent S3 interface with support for modeling scale parameters.
  - Multiple variance-covariance estimators (OIM, OPG, robust,
    cluster-robust, bootstrap, jackknife).
  - Full suite of post-estimation tools and hypothesis tests.
  - Compatible with marginaleffects for marginal effects and
    predictions.
  - Comprehensive vignettes covering main model families and
    diagnostics.