Quick start

import pymice
from pymice import mice, complete, with_mids, pool, summary_pool

data, names = pymice.load_nhanes()
imp = mice(data, column_names=names, seed=123)

fit = with_mids(imp, formula="bmi ~ age + hyp + chl")
print(summary_pool(pool(fit)))

R-aligned imputations

Requires Rscript and CRAN package mice:

imp_r = mice(data, column_names=names, seed=123, rng="r")

Parallel chains

from pymice import futuremice

imp_par = futuremice(
    data, column_names=names, m=5, parallelseed=123, n_core=2, print=False
)

See dev/REPRODUCIBILITY.md for RNG backends and publication reporting.