Introduction | Quick-start: MI in 10 lines | Missing-Data Mechanisms | MCAR: Missing Completely at Random | MAR: Missing at Random | MNAR: Missing Not at Random | Practical guidance | Multiple Imputation: Theory | The MI procedure | Rubin's rules | Degrees of freedom | Diagnostic quantities | Why MI works | Proper imputation | Congeniality | Multiple Imputation with miceFast | Why miceFast for MI? | Stochastic models for MI | Basic MI workflow | MI with mixed variable types | MI with GLMs | MI with grouped imputation | MI with weighted imputation | MI with PMM (OOP interface) | Sensitivity Analysis | Why sensitivity analysis? | Comparing models with fill_NA_N() | Varying the number of imputations | Comparing with base methods | Choosing the Number of Imputations | Practical Checklist | Von Hippel's two-stage rule for m | Comparison with mice | References

ML weights | Multi-period chaining | Optional: passing mappings$freqs_df | Mapping table stability and truncation | Backward chaining | Forward chaining | Adding ML to the chain | Panel data with subject identifiers | Aggregated data and special cases | Regression on replicated data | Building a 4-period harmonised repeated cross-section dataset | Neutral impact demonstration | Fixed effects regression | Choosing the right advanced workflow | Next steps

Step 1: Understand the competing weight assumptions | If ML fails on some rows: on_fail and fail_warn | Step 2: Check whether conclusions are sensitive to the weight choice | Compare weight methods on the same mapped data | Compare pruning strategies only after comparing full weights | Compare ensemble compositions when no single method dominates | Step 3: Validate whether ML actually improves on simpler baselines | What cat2cat_ml_run() is doing | Minimal validation workflow | Compare multiple ML methods in one run | Inspect per-group diagnostics when methods disagree | Decision rules for interpreting the output

What is cat2cat? | How to read the documentation | The Problem | The Solution | Value Added of cat2cat | Harmonisation Assumptions and Sensitivity | Three weighting schemes | When each assumption matters | Reducing assumption dependence | When cat2cat won't help | If you need something more advanced | Use Cases | Key Concepts | Direction: Backward vs Forward | ML weights in practice | Weights | Quick Example | Forward mapping example | Naive vs Frequency Weights | Value added: pooled regression across both periods | Diagnostic Plot | Mapping table | Common hierarchical classifications | Truncating mapping tables from hierarchical codes | Learn More

Overview | Available Imputation Models | Example Data | Examining Missingness | Checking for Collinearity | Single Imputation with fill_NA() | Basic usage (dplyr) | With weights and grouped imputation | Log-transformation | Using column position indices | Basic usage (data.table) | Multiple Imputation with fill_NA_N() | dplyr | data.table with grouping | Comparing Imputations | Multiple Imputation with pool() | Full Imputation: Filling All Variables and MI with Rubin's Rules | The miceFast OOP Module | Methods | Simple example | With weights and groups | With unsorted groups | Full MI workflow with OOP | Iterative FCS (Chained Equations) with miceFast | data.table (convenience functions) | Generating Correlated Data with corrData | Tips | References

Introduction | tinyverse badges | tinyverse vs tidyverse | R package Dependencies | tinytest vs testthat | How to reduce the number of dependencies | Delayed loading of packages | pacs package | References

Hints | Validate the library | Investigate by filtering | Core idea behind lib_validate | renv library | renv lock file | R CRAN packages check page statuses | Package health and life duration | Simulate a package download with the withr package | Per package utils | Time Machine | Package dependencies | Package DESCRIPTION file | Package NAMESPACE file | Compare different package versions | Comparing DESCRIPTION files between different package versions | Comparing NAMESPACE files between different package versions | Package size | The shiny app utils | Base packages