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

Built-In Structured Documentation | DeepWiki | JSS Article | Cheatsheet | Vignettes | Blog | Presentations and Slides

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

Overview of Both Packages | Interoperating and some Do's and Dont's | Further collapse features supporting data.table's | Additional Benchmarks | References

1. Fast Aggregations | 1.1 Simple Aggregations | Excursus: What is Happening Behind the Scenes? | 1.2 More Speed using collapse Verbs | 1.3 Multi-Function Aggregations | 1.4 Weighted Aggregations | 2. Fast Transformations | 2.1 Fast Transform and Compute Variables | 2.2 Replacing and Sweeping out Statistics | 2.3 More Control using the TRA Function | 2.4 Faster Centering, Averaging and Standardizing | 2.5 Lags / Leads, Differences and Growth Rates | 3. Benchmarks | 3.1 Data | 3.1 Selecting, Subsetting, Ordering and Grouping | 3.1 Aggregation | 3.2 Transformation | References

Part 1: Fast Transformation of Panel Data | 1.1 Between and Within Transformations | 1.2 Higher-Dimensional Between and Within Transformations | 1.3 Scaling and Centering | 1.4 Panel Lags / Leads, Differences and Growth Rates | 1.5 Panel Data to Array Conversions | Benchmarks | Part 2: Fast Exploration of Panel Data | 2.1 Variation Check for Panel Data | 2.2 Summary Statistics for Panel Data | 2.3 Exploring Panel Data in Matrix / Array Form | 2.4 Panel- Auto-, Partial-Auto and Cross-Correlation Functions | 2.5 Testing for Individual Specific and Time-Effects | Part 3: Programming Panel Data Estimators | 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

Why collapse? | 1. Data and Summary Tools | 1.1 wlddev - World Bank Development Data | 1.2 GGDC10S - GGDC 10-Sector Database | 2. Fast Data Manipulation | 2.1 Selecting and Replacing Columns | 2.2 Subsetting | 2.3 Reordering Rows and Columns | 2.4 Transforming and Computing New Columns | 2.5 Adding and Binding Columns | 2.6 Renaming Columns | 2.7 Using Shortcuts | 2.8 Missing Values / Rows | 2.9 Unique Values / Rows | 2.10 Recoding and Replacing Values | 3. Quick Data Object Conversions | 4. Advanced Statistical Programming | 4.1 Fast (Grouped, Weighted) Statistical Functions | 4.2 Factors, Grouping Objects and Grouped Data Frames | 4.3 Grouped and Weighted Computations | 4.4 Transformations Using the TRA Argument | 5. Advanced Data Aggregation | 6. Data Transformations | 6.1 Row and Column Arithmetic | 6.1 Row and Column Data Apply | 6.2 Split-Apply-Combine Computing | 6.3 Fast (Grouped) Replacing and Sweeping-out Statistics | 6.4 Fast Standardizing | 6.5 Fast Centering and Averaging | 6.6 HD Centering and Linear Prediction | 7. Time Series and Panel Series | 7.1 Panel Series to Array Conversions | 7.2 Panel Series ACF, PACF and CCF | 7.3 Fast Lags and Leads | 7.4 Fast Differences and Growth Rates | 8. List Processing and a Panel-VAR Example | 8.1 List Search and Identification | 8.2 List Subsetting | 8.3 Recursive Apply and Unlisting in 2D | Going Further | References

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