Cell data | Plotting | Choosing the display resolution | Zooming in | Map projections | Large maps | Summarizing without plotting

Grids | Resolutions | Cells | Geometry | Neighborhoods | Changing scale | Saving and restoring cells | Under the hood

From raster to cells | How pixels are summarized | Into cells you already have | Rasters that cover the globe | From polygons to cells | Back to sf

Overview | Setup | Tuning EOF truncation | Tuning CCA downscaling | Preparing folds | Grid search | Tuning k_cca separately | Fold construction | Parallel execution | Best practices

Load the Toolkit | Load a Model | Detection Workflow | Predict a Single Image | Review the Output Columns | Summarize a Single Image | Batch Process a Directory | Summarize Across Images | Plot Detection-Level Distributions | Segmentation Workflow | Inspect Morphology Columns | Single-Image Morphology Summary | Plot Morphology | Batch Segmentation Analysis | Summaries from Batch Segmentation | Choosing an Analysis Path | Next Steps

Choose a Training Task | Prepare the Dataset | Optional: Slice Large Images | Optional: Pin the Dataset | Start Training | Detection Example | Segmentation Example | Local vs HPC Training | Local Training | HPC Training | Choose a Model Variant | What Training Produces | Evaluate and Inspect the Result | Detection Validation | Segmentation Validation | Maintainership and Publishing | Troubleshooting

Load the Toolkit | Point to a Large Image | Load a Pretrained Model | Run SAHI Inference | Review the Overlay | Summarize Detections | Batch Process a Directory | Next Steps

Overview | Example data | Climatology and anomalies | Extracting patterns | Plotting | Scree plot and significance | Rotation | Subsetting patterns | Reconstruction | Projection | Teleconnections | Area weighting | Overlays | References

Overview | Setup: predictor and response data | Step 1: Extract patterns | Step 2: Couple with CCA | Diagnostics | Step 3: Predict | Amplitudes only | Common patterns for multi-source downscaling | Next steps | References

Introduction | Installation | Creating a Vector Collection | Generating Embeddings | Finding Similar Items | Combining with Tidyverse Operations | Persistence | Hybrid Search | Clustering | Working with Images | Visualizing Embedding Spaces | Advanced Use Cases | RAG (Retrieval-Augmented Generation) | Custom Embedders | Conclusion