# enmpa 0.2.5
================================================================================

- **Bug fixed**:
  - Fixed: Bug in response curves with fitted models with one variable.
# enmpa 0.2.4
================================================================================

- **Bug fixed**:
  - Fixed: Replace Rf_error with `Rcpp::stop` ([Calls to Rf_error #11](https://github.com/Luisagi/enmpa/issues/11)).
 
# enmpa 0.2.3
================================================================================

- **Bug fixed**:
  - Fixed bug in predict_selected related to the 'AICw' rounded values.
  
# enmpa 0.2.2
================================================================================

- **Functions update**:
  - add flag "show_lines" (Default = TRUE) in response_curve() function.

# enmpa 0.2.1
================================================================================

- **Bug Fixes**:
  - Fixed bug in plot_niche_signal related to the 'lwd' argument handling.
  - Fixed bug in plot_importance that caused an error when the model list contained only one model.

# enmpa 0.2.0
================================================================================

- **Bug Fixes**:
  - `var_importance()` now generates a bar plot even when the model list
  contains a single model, instead of throwing an error.

  - `get_formulas()` now returns the correct count of generated formulas when
  `mode = "intensive"`.
  
# enmpa 0.1.9
================================================================================

- **New Functions**:
  - Added three new functions: `resp2var()`,  `jackknife()`, and `plot_jk()`.
    - `resp2var()`: Transforms species probability data into a two-dimensional environmental space for visualization.
    - `jackknife()`: Evaluates the influence of each variable on the overall model using four distinct metrics: ROC-AUC, TSS, AICc, and Deviance. This function facilitates jackknife resampling to assess variable importance.
    - `plot_jk()`: A function to plot the results of the jackknife resampling.
  
- **Bug Fixes**:
  - Fixed a bug in `calibration_glm()` related to runtime calculation errors.
  
# enmpa 0.1.8
================================================================================

- **New Classes**:
  - Added two new classes: `enmpa_calibration` and `enmpa_fitted_models`.
    - These classes help manage the list outputs from the functions `calibration_glm` and `fit_selected`.
    - Each class has two associated methods: `summary()` and `print()`, which provide summaries and print representations of the objects, respectively.

- **Updates to `predict_glm`**:
  - Added a new flag `extrapolation_type` to indicate the type of extrapolation:
    - `"E"`: Free extrapolation
    - `"NE"`: No extrapolation
    - `"EC"`: Extrapolation with clamping
  - The flag `var_to_clamp` was replaced by `restricted_vars`.
  - The flag `clamping` was removed.

- **Updates to `model_validation`**:
  - Now includes 'residual deviance' as a validation metric.

# enmpa 0.1.5
================================================================================

- Initial CRAN submission.
