Summary | Description | Getting ready | Preparing the Bias Layer | Function Arguments | Example: Mixed-Direction Composite Bias | Applying Bias to Predictions | Function Arguments | Example: Comparing Applied Biases | Three-Dimensional Example | Save and export

Summary | Description | Getting ready | Loading example data | Building and visualizing ellipsoid niches | Background | Creating a basic ellipsoid | Adjusting ellipsoid covariance | Comparing multiple species niches | Save and import | Working in more than 2 dimensions

Summary | Description | Getting ready | Part 1: Virtual Data | Basic generation | Visualizing virtual data in 2D | Three-dimensional virtual example | Part 2: Spatially-Explicit Occurrence Data | Basic generation in 2D | Effect of the sampling argument | Effect of the method argument | Effect of the strict argument | Three-Dimensional Example | Part 3: Biased Occurrence Data | Basic biased generation in 2D | Effect of the biased strict argument | Three-Dimensional Biased Example | Save and export

Summary | Description | Getting ready | Loading example data | Using predict() | Basic predictions to a data frame | Basic predictions to a SpatRaster | Understanding the output | Mahalanobis distance and the ellipsoid | From distance to suitability: the chi-square and MVN connection | Additional function arguments | All four outputs at once | The role of the confidence level and truncation | Effect of the confidence level on predictions | Visualizing predictions in environmental space | Mahalanobis distance in E-space | Suitability in E-space | Truncated predictions in E-space | Binary suitable vs. unsuitable environments | Visualizing predictions in geographic space | Mahalanobis distance map | Suitability map | Truncated suitability map | Binary potential distribution map | Three-dimensional example | Predicting with virtual data | Two-dimensional virtual data | Three-dimensional virtual data | Save and import

Summary | Description | Getting ready | Loading example data | Simulating random communities | Effect of background density | Effect of proportion arguments | Simulating nested communities | Effect of proportion argument | Effect of bias argument | Simulating niche conservatism in communities | Predictions for communities | Predict to data frames | Predict to SpatRaster | Truncating predictions | Simple community outcomes | Save and import

Summary | Description | Getting ready | Loading example data | plot_ellipsoid() | Ellipsoid boundary only | Background points | Prediction colored by a continuous variable | Truncated predictions and grey outside region | Reversed palette and transparency | Subsampling large datasets | Fixed axis limits for comparing ellipsoids | add_data() and add_ellipsoid() | Layering background, occurrences, and boundary | Coloring occurrence points by a continuous variable | plot_ellipsoid_pairs() | Pairs with background | Pairs with predictions

Summary | Description | Getting ready | Preparing the Bias Layer | Function Arguments | Example: Mixed-Direction Composite Bias | Applying Bias to Predictions | Function Arguments | Example: Comparing Applied Biases | Three-Dimensional Example | Save and export

Summary | Description | Getting ready | Loading example data | Building and visualizing ellipsoid niches | Background | Creating a basic ellipsoid | Adjusting ellipsoid covariance | Comparing multiple species niches | Save and import | Working in more than 2 dimensions

Summary | Description | Getting ready | Part 1: Virtual Data | Basic generation | Visualizing virtual data in 2D | Three-dimensional virtual example | Part 2: Spatially-Explicit Occurrence Data | Basic generation in 2D | Effect of the sampling argument | Effect of the method argument | Effect of the strict argument | Three-Dimensional Example | Part 3: Biased Occurrence Data | Basic biased generation in 2D | Effect of the biased strict argument | Three-Dimensional Biased Example | Save and export

Summary | Description | Getting ready | Loading example data | Using predict() | Basic predictions to a data frame | Basic predictions to a SpatRaster | Understanding the output | Mahalanobis distance and the ellipsoid | From distance to suitability: the chi-square and MVN connection | Additional function arguments | All four outputs at once | The role of the confidence level and truncation | Effect of the confidence level on predictions | Visualizing predictions in environmental space | Mahalanobis distance in E-space | Suitability in E-space | Truncated predictions in E-space | Binary suitable vs. unsuitable environments | Visualizing predictions in geographic space | Mahalanobis distance map | Suitability map | Truncated suitability map | Binary potential distribution map | Three-dimensional example | Predicting with virtual data | Two-dimensional virtual data | Three-dimensional virtual data | Save and import

Summary | Description | Getting ready | Loading example data | Simulating random communities | Effect of background density | Effect of proportion arguments | Simulating nested communities | Effect of proportion argument | Effect of bias argument | Simulating niche conservatism in communities | Predictions for communities | Predict to data frames | Predict to SpatRaster | Truncating predictions | Simple community outcomes | Save and import

Summary | Description | Getting ready | Loading example data | plot_ellipsoid() | Ellipsoid boundary only | Background points | Prediction colored by a continuous variable | Truncated predictions and grey outside region | Reversed palette and transparency | Subsampling large datasets | Fixed axis limits for comparing ellipsoids | add_data() and add_ellipsoid() | Layering background, occurrences, and boundary | Coloring occurrence points by a continuous variable | plot_ellipsoid_pairs() | Pairs with background | Pairs with predictions