PyMICE documentation¶
PyMICE implements Multivariate Imputation by Chained Equations (MICE / FCS) in Python with algorithmic alignment to the R mice reference.
User guide¶
- Install
- Quick start
- Vignette walkthroughs — Python walkthroughs of the official R
micetutorials (V01–V08); start with V1: Ad Hoc MICE
Vignette quick links¶
| # | Topic | Start here |
|---|---|---|
| V1 | Ad Hoc MICE | Recommended first tutorial |
| V2 | Convergence & pooling | Traces and pool() |
| V3 | Missingness models | Patterns and inspection |
| V4 | Passive imputation | Passive & post-processing |
| V5 | Multilevel data | Clustered imputation |
| V6 | Sensitivity analysis | δ adjustment & survival |
| V7 | Ampute (appendix) | Simulate missingness |
| V8 | Parallel MICE (appendix) | futuremice workflow |
Full parity table, learning path, and pytest status: vignettes/index.html. - Citing PyMICE
Maintainer / parity¶
Development tooling, R golden snapshots, and parity status live under dev/.
Package reference¶
API documentation is generated from docstrings. Build locally:
bash devtools/setup_venv.sh
source ~/.venvs/brain-pymice/bin/activate
pip install -e ".[dev,docs]"
mkdocs serve
Published site: ryanpmcg.github.io/PyMICE (MkDocs + vignette HTML under /vignettes/).