                        Changes in version 1.2.5                        

New features

  - Added a new vignette, Model Selection.
  - A model_selection function was created to automate the model
    selection process.
  - New default option for performance. "AICc" was set as default.

                 Changes in version 1.2.4 (2026-08-21)                  

New features

  - Added a new vignette, Modeling soybean canopy cover.
  - Added the example dataset dt_soybean_22, containing soybean
    canopy-cover time-series data from Keller et al. (2026).
  - The print() method for modeler objects now reports the parameter box
    constraints used during model fitting.

Bug fixes

  - plot.modeler() no longer produces the "Ignoring unknown labels"
    message with ggplot2 >= 4.0 for plot types 4, 5, and 6. Legend
    labels are now added only when the corresponding aesthetics are
    mapped.

                 Changes in version 1.2.3 (2026-06-11)                  

New features

  - New example dataset dt_potato_22

Bug fixes

  - Fixed issue with plot.performance() when reporting average metrics.
  - Fixed minor bugs and issues.

                 Changes in version 1.2.2 (2026-01-27)                  

New features

  - New functions added: fn_lpl(), fn_qpl(), fn_qpl().

                 Changes in version 1.2.1 (2025-11-04)                  

Changes

  - compute_tangent() accepts the x argument as data.frame.

Bug fixes

  - Fixed issue when ggplot2 was updated.
  - Parallel was not working properly.

                 Changes in version 1.2.0 (2025-04-16)                  

New features

  - compute_tangent() function added to compute tangent line(s) for a
    modeler object.
  - inverse_predict.modeler() S3 method added to calculate inverse
    predictions for modeler objects.
  - update.modeler() S3 method added to refit a model of class modeler.
  - Adding fn_lin_logis(), fn_quad_plat() and fn_quad_pl_sm().
  - predict.modeler() includes parallel and workers to allow for
    parallel computing.

Changes

  - When evaluating several methods in modeler(), Jacobian and Hessian
    are computed only for the best method.
  - Now functions are required to be vectorized (faster execution).
  - Renaming fn_lin_plat() function.
  - The modeler() function now uses optimr instead of opm for faster
    execution.
  - plot.modeler() includes linewidth argument to increase size in geom
    lines.

Bug fixes

  - Removed methods that required hessian matrix (snewton, snewtonm,
    snewtm) in list_methods().
  - Fixed issue when combining fitted values in modeler().

                 Changes in version 1.1.0 (2025-02-21)                  

New features

  - fitted.modeler() S3 method added to extract fitted values from
    modeler objects.
  - residuals.modeler() S3 method added to extract residuals from
    modeler objects.
  - augment() function added to calculate influence measures (Cook's
    distance, leverage values, standardized residuals, studentized
    residuals).
  - c.modeler() S3 method added to combine modeler objects.
  - subset.modeler() S3 method added to subset modeler objects.
  - performance() function added to evaluate the performance of several
    models.
  - plot.performance() S3 method to plot an object of class performance.

Changes

  - modeler() adds the function name (fn_name) in every output table.
  - modeler() no longer returns function call.
  - plot.modeler() includes add_ribbon_pi and add_ribbon_ci arguments
    for prediction and confidence intervals.
  - metrics() returns R2 instead of r_squared.

Bug fixes

  - Fixed conflict of modeler() with upcoming version of future.
  - Fixed increase dependency to R (>=4.1).
  - Fixed regression function not found in the environment when running
    in parallel.

                 Changes in version 1.0.0 (2025-01-20)                  

                        Changes in version 0.1.0                        

  - Initial CRAN submission.
