Changes in version 2.1.1 (2025-10-01)                  

  - Examples now use fewer iterations, reducing test run time.
  - Replaced is.ggplot(p1) with is_ggplot(p1) in tests.
  - Added [] in documentation for package references.
  - Removed docType: Package from documentation.

                 Changes in version 2.1.0 (2024-02-05)                  

  - Support only for R >= 4.0.0
  - Updated README
  - Added support for ordinal regression, using clm

                 Changes in version 2.0.0 (2020-12-12)                  

Important API Change:

  - All submodels in fit functions are now calculated using update(),
    ensuring that all settings of the original model are used in the
    dominance analysis. If R cannot find the original dataset for
    update, provide the dataset using the newdata parameter in the
    dominanceanalysis() method.

Minor Updates:

  - Minor modification in dominanceMatrix.
  - Added complete_flipped_axis parameter for the plot() function.
    Included examples of flipped axis for plots.
  - Tests for bootstrap analysis (betareg, lmm, glm).
  - Added Nakagawa's indexes for linear mixed models. Refer to README
    for examples.
  - In the beta regression test, we check that using different
    estimators generates different results.

Bug Fixes:

  - On R-devel, corrected the number of controls provided for the esoph
    dataset, fixing a test based on that example.
  - Replaced sort.matrix with .sort.matrix to prevent sorting a standard
    matrix when devtools are used.
  - Fixed documentation error in plot.dominanceAnalysis: the default
    plot is for general dominance, not for complete dominance.

                 Changes in version 1.3.0 (2020-01-08)                  

  - Added plot() method for dominanceAnalysis objects.
  - Added support for beta regression using the betareg package.
    Suggested by Maartje Hidalgo.
  - Added 'ordered' parameter to the dominanceMatrix() method.
  - Bug fix: Corrected errors in the logistic regression vignette from
    v1.2.0 and added some plots for clarification.

                 Changes in version 1.2.0 (2019-09-22)                  

  - Added documentation for da.dynlm.fit.
  - Test for the esoph example.
  - New Acknowledgments section in the README file.
  - Removed dependencies on caTools and heplots, as they are only used
    in examples.
  - Bug fix: Fixed issue where logistic regression indexes failed in
    some models. Null model likelihood is now correctly calculated using
    update(x, ~1). Reported by Daniel Schlaepfer.
  - Added basic support for dynamic linear models using the dynlm
    package. Suggested by Xiong Zhang.

                        Changes in version 1.1.0                        

  - Support for named terms in bootDominanceAnalysis and
    bootAverageDominanceAnalysis.
  - Added dominanceBriefing().

                 Changes in version 1.0.0 (2019-01-30)                  

  - First official version. Code coverage of 99% and complete
    documentation for all methods.
  - Added vignette "Exploring Predictors' Importance in Binomial
    Logistic Regressions" by Filipa Coutinho Soares.
  - Introduced dominanceMatrix() as a generic for matrix, data.frame,
    and dominanceAnalysis methods.
  - New retrieval methods for the dominanceAnalysis object: getFits(),
    contributionAverage(), and contributionByLevel().
  - Removed support for nlme (it never worked well).

                        Changes in version 0.1.2                        

  - Added terms parameter in dominanceAnalysis to manually define a set
    of variables.
  - Updated documentation, thanks to Filipa Coutinho Soares.
  - Test for all relevant functions.

                        Changes in version 0.1.1                        

  - Allowed multivariate regression with a covariance matrix.
  - Added documentation for multivariate methods.
  - Added bootstrap analysis for average contribution.
  - Resolved bugs in bootstrap analysis and glm.da.fit.

                        Changes in version 0.1.0                        

  - Complete support for OLS, logistic regression, and HLM.
  - Support for bootstrap analysis.