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The leather dataset | Building an icio object | Leontief decomposition | Koopman-Wang-Wei (KWW) decomposition | Wang-Wei-Zhu (WWZ) decomposition | Borin-Mancini (BM) decomposition | Country level (exporter perspective, 13 terms) | Sector level | Bilateral sector level | Corrected KWW (world perspective, 9 terms) | WWZ vs. BM (exporter/source): different perimeters | Convenience interface | Output format | References

Determining the Structure of the Model | Estimation and Exploration | Forecasting | Estimation with Mixed Frequency | Modeling AR(1) Idiosyncratic Errors | News Decomposition | Additional Functions | Conclusion | References

Introduction | Getting Started | Basic Workflow Example | Step 1: Visualize the Network | Step 2: Convert Network to Graph | Step 3: Extract Nodes and Map Cities | Step 4: Create OD Matrix | Step 5: Run Traffic Assignment | Step 6: Visualize Results | Advanced Workflow: Trade Flow Disaggregation | Step 1: Compute City Population Shares | Step 2: Disaggregate Trade Flows | Step 3: Run Assignment with Trade Flows | Advanced Workflow with Network Consolidation | Using Raw Segments | Consolidate Graph | Compare Original vs Consolidated Network | Network Simplification with simplify_network() | Shortest-Paths Method | Cluster Method | Key Concepts and Functions | Path-Sized Logit Model | Probability Formula | The Correction Term: Path-Size Factor ($PS_k$) | Network Processing Functions | Graph Utilities | OD Matrix Utilities | Traffic Assignment | Parameter Tuning Tips | Conclusion | Next Steps | References

Overview | Parallel Statistical Functions | Row-wise Arithmetic | Row-wise Min, Max, and Range | Coalescing Values | Logical and Count Operations | Vectorized Conditionals | Fast If-Else (iif) | Nested Conditionals (nif) | Vectorized Switch (vswitch, nswitch) | Fast Unique and Duplicates | Unique Values and Duplicates | Counting Occurrences | Sorting and Utilities | Partial Sorting (topn) | Factor Manipulation | Finding Positions (fpos) | Summary

Spatial Analysis Done Right | Grids | ISEA3H Details | How do I use it? | Examples | Binning Lat-Long Points | Randomly Sampling the Earth: Method 1 | Randomly Sampling the Earth: Method 2 | Save a grid for use in other software | Get a grid that covers South Africa | Caveats | Roadmap | Credits | Disclaimer | Citing this Package | References

Summarising sf Data Frames | Selecting Columns and Subsetting | Aggregation and Grouping | Indexing | Unique Values, Ordering, Splitting, Binding | Transformations | Conversion to and from sf | Support for units | Conclusion

Namespace and Global Options | Using the Fast Statistical Functions | Writing Efficient Code | Using Internal Grouping | Conclusion

Overview | General Principles | Specific Functions and Classes | Object Conversions | Selecting Columns by Data Type | Parsing of Time-IDs | xts/zoo Time Series | Support for sf and units | Support for data.table | Class-Agnostic Grouped and Indexed Data Frames | Conclusion

Introduction | Point 1: Be Minimalistic in Computations | Point 2: Think About Memory and Optimize | Point 3: Internally Favor Primitive R Objects and Functions | Some Notes on Global Options | Conclusion

Extending the fastverse for the Session | Permanent Extensions | Custom fastverse Configurations for Projects | Creating Separate Package-Verses | Dependencies, Situational Reports and Updating | Other fastverse Options | Conclusion | Appendix: A note on OpenMP Multithreading