accmv
Python implementation of inverse-probability-weighted (IPW), regression-adjustment (RA), and multiply-robust (MR) inference under the available complete-case missing value assumption.
from accmv import estimate_single
fit = estimate_single(x, y, method="mr", n_boot=499, random_state=1)
print(fit.estimate, fit.conf_int)
For the paper's marginal linear model:
from accmv import fit_ipw_regression
# Shared with R: response/predictors use one-based y-column indices.
fit = fit_ipw_regression(x, y, response=2, predictors=(1,))
print(fit.coefficients)
If you use this software, please cite Cheng, Chen, Smith, and Zhao, “Handling Nonmonotone Missing Data with Available Complete-Case Missing Value Assumption” (paper).
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Release files for accmv 0.1.1
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| accmv-0.1.1.tar.gz | 51.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| accmv-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 61.7 kB
Release files / accmv-0.1.1.tar.gz
| Download URL | accmv-0.1.1.tar.gz |
|---|---|
| Size | 51.4 kB |
| Tags | Source |
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Transparency logRelease files / accmv-0.1.1-py3-none-any.whl
| Download URL | accmv-0.1.1-py3-none-any.whl |
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| Size | 10.3 kB |
| Tags | Python 3 |
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