Setup | Training diagnostics | Pair inspection | Lazy prediction for large data | Chunked u estimation and SQL profiling | Cluster diagnostics | Phonetic blocking | Column transforms | Incremental matching | Cleanup

Setup | Explore the data | Choose blocking rules | Define the specification | Train the model | Inspect the trained model | Save and reuse the model | Predict and cluster | Evaluate against ground truth | Accuracy metrics | ROC curve | Precision-recall curve | Error inspection | Unlinkables | Cleanup

What is record linkage? | A typical workflow | Step 1: Define a specification | Step 2: Build a model | Step 3: Train parameters | Step 4: Predict | Step 5: Cluster | Comparison levels | Evaluation | Cleaning up

Setup | Load the data | Explore data quality | Define the specification | Train the model | Inspect the model | Predict and cluster | Evaluate against ground truth | Accuracy metrics | ROC and Precision-recall | Cleanup

Design differences | Core workflow | Comparison functions | Key parameters | Example: side-by-side deduplication | Blocking | Model inspection | Evaluation

Design differences | Core workflow | Comparison levels | Domain-specific comparisons | Model inspection | Evaluation | Data profiling | Persistence | Blocking rules | Example: side-by-side deduplication | Example: finding matches against new records

Area-Based Compactness | Polsby-Popper Compactness | Schwartzberg Compactness | Reock Compactness | Box Reock Compactness | Bounding Box Reock Compactness | Convex Hull Compactness | Skew Compactness | X-Symmetry Compactness | Y-Symmetry Compactness | Shape-Based Compactness | Holes Compactness | Components Compactness | Corners Compactness | Boundary Jaggedness Compactness | KIWSI Compactness | Distance-Based Compactness | Length-Width Compactness | Boyce-Clark Compactness | Fryer Holden Compactness | Adjacency-Based Compactness | Edges-Removed Compactness | Fraction Kept Compactness | Log Spanning Tree Compactness | General Notes on Speed

Setup | Training diagnostics | Pair inspection | Lazy prediction for large data | Chunked u estimation and SQL profiling | Cluster diagnostics | Phonetic blocking | Column transforms | Incremental matching | Cleanup

Setup | Explore the data | Choose blocking rules | Define the specification | Train the model | Inspect the trained model | Save and reuse the model | Predict and cluster | Evaluate against ground truth | Accuracy metrics | ROC curve | Precision-recall curve | Error inspection | Unlinkables | Cleanup

What is record linkage? | A typical workflow | Step 1: Define a specification | Step 2: Build a model | Step 3: Train parameters | Step 4: Predict | Step 5: Cluster | Comparison levels | Evaluation | Cleaning up

Setup | Load the data | Explore data quality | Define the specification | Train the model | Inspect the model | Predict and cluster | Evaluate against ground truth | Accuracy metrics | ROC and Precision-recall | Cleanup

Design differences | Core workflow | Comparison functions | Key parameters | Example: side-by-side deduplication | Blocking | Model inspection | Evaluation

Design differences | Core workflow | Comparison levels | Domain-specific comparisons | Model inspection | Evaluation | Data profiling | Persistence | Blocking rules | Example: side-by-side deduplication | Example: finding matches against new records

Overview | Setup | Inspect data | Define population targets | Rake | Inspect results | Design effect | Per-variable diagnostics | Tidy output | Augmenting the data | Advanced options | Weight bounding | Variable selection | Checking discrepancies directly

What is BCP 47? | Parsing | Language Matching | Validation | Normalization | The IANA Registry | Caching

One-step helpers | Legend-only helpers | Scale specifications | Working with scales | Labels and breaks | Palette functions | Date and time data | Legend placement | Output support

Overview | Data | Quick start with rict() | Specialized tables | Population parity | Demographics | Elections | Compactness | Contiguity | Administrative splits | Component populations | Getting tibbles instead of gt tables | Extending gt tables | Partisan color scales | Geometry plots | Compactness plots | Other gt utilities

Installation | Installing from a Manifest | Creating and Using a Manifest | Creating a Minimal Manifest | Example Manifest Files | Basic project manifest | Adding optional dependency groups | Including a package from GitHub | Using a local package path | Validating a Manifest | Example Workflow | 1. Creating a new manifest with common dependencies | 2. Installing core and dev dependencies | 3. Installing all dependencies | Manifest File Structure | Converting from DESCRIPTION to Manifest | Converting from Manifest to DESCRIPTION | Learn More

The redist package | Algorithms | Data | Compiling, cleaning, and preparing the data | Iowa | Defining the redistricting problem | Determining the relevant constraints | Setting up the problem in redist | Adjacency-based redistricting | Pre-processing | Exploring the geography | Simulating redistricting plans | Analyzing the simulated plans

The Map | Creating the redist_map object | Subsetting | Specific districts | Dealing with water and islands | Merging | Freezing | District Cores

The dots Package

Aggregating Population Data to Precincts | Disaggregating and Reaggregating

Democratic Vote Share | Democratic Seats | Partisan Bias | Responsiveness | Declination | Simplified Declination | Mean-Median Score | Lopsided Wins | Efficiency Gap | Efficiency Gap (Equal Population) | Tau Gap | Dilution Asymmetry | Smoothed Seat Count Deviation | Ranked Marginal Deviation | Partisan Dislocation

Introduction | Authentication | Pagination | Filtering by Date | Retrieving Legislation | Legislative Details | Member Activity | Committee Work | Executive Business | Congressional Record | Summary

Creating a New Post | Creating a Post with a Quote | Posting with Media and Links | Replying to a Post | Liking a Post | Reposting a Post | Deleting Content (Posts, Likes, and Reposts) | Advanced: Using bs_create_record() | Conclusion

Introduction | Fetching and Exploring Lists | Creating and Managing Lists | Understanding Starter Packs | Fetching Starter Packs | Creating and Managing Starter Packs | Conclusion

Setup and Authentication | Working with your own data | Public Data about Other Users | Accessing Profile Information | Gathering Posts from a User | Retrieving Threads and Replies | Accessing Social Graph Data | Likes and Reposts | Conclusion

Table of Contents | The Flip MCMC Algorithm | Strengths of Flip | Initializing Flip | Using Multiple Chains | Tidy Flip MCMC using redist_flip() | Diagnostic Plots | Tuning Flip Constraints | Acceptance Ratios | lambda and eprob | Adjusting pop_tol | Balancing Multiple Constraints | Some Final Thoughts

Installation | Running your first measure

The P.L. 94-171 Legacy File | Using the PL94171 package

Overview: 2x2 R-commands | ei: To run the main procedure | summary: To obtain a general summary of district-level information | plot: To graph relevant information | eiread: To obtain relevant information and numerical results | User's Guide: An Example | The Basic EI Algorithm | Extracting Quantities of Interest | Plotting in EI | References

adj | ndists | nsims | pop_tol | plan | plans | init_plan | total_pop | group_pop | pop_bounds | ncores

Introduction: Ecological Inference

Overview:eiRxC R-commands | eiRxC Tomography Plots: Assessing Model Dependence | References

Competitiveness | Talismanic Compactness | Segregation | Dissimilarity | Incumbents | Incumbent Pairings

Administrative Splits | Sub-Administrative Splits | Mutli-Splits | Counting Splits

Variation of Information | Hamming Distance | Manhattan Distance | Euclidean Distance