User Guide
==========

This guide provides comprehensive documentation for using mice-py effectively in your data analysis workflows.

.. toctree::
   :maxdepth: 2

   understanding_missing_data
   mice_overview
   imputation_methods
   predictor_matrices
   convergence_diagnostics
   pooling_analysis
   best_practices

Overview
--------

The user guide covers all aspects of using mice-py, from understanding missing data 
to producing final pooled results. Each section builds on the previous ones, but you 
can also jump directly to topics of interest.

**Understanding Missing Data**
   Learn about missing data mechanisms, patterns, and why they matter for your analysis.

**MICE Overview**
   Understand how the MICE algorithm works and when to use it.

**Imputation Methods**
   Detailed guide to all available imputation methods and how to choose the right one.

**Predictor Matrices**
   Learn how to control which variables predict which other variables during imputation.

**Convergence Diagnostics**
   Check if your imputation has converged and what to do if it hasn't.

**Pooling Analysis**
   Combine results from multiple imputed datasets using Rubin's rules.

**Best Practices**
   Tips, tricks, and common pitfalls to avoid when using MICE.

Getting Help
------------

If you need help:

1. Check the relevant section in this user guide
2. Look at the :doc:`../examples/index` for practical examples
3. Consult the :doc:`../api/index` for detailed API documentation
4. Review the :doc:`../theory/index` for theoretical background
5. Open an issue on `GitHub <https://github.com/Zhanna-Lopuliak/mice-py/issues>`_