AShrink

AShrink provides empirical Bayes shrinkage for noisy effect estimates, count rates, binomial probabilities, effect estimates measured across several conditions, and regression from outcomes and predictors, including Gaussian random intercepts. It learns finite-mixture weights from the data and returns posterior summaries.

Documentation · Source

The adaptive-shrinkage method follows Stephens (2017). The R packages ashr and mashr are numerical references for matched methods. Count models use conjugate mixtures with a specified common mode. AShrink is an independent implementation; the original authors have not endorsed it.

Install from source

python -m pip install 'git+https://github.com/nilson01/ashrink.git'

From a local checkout, use python -m pip install ..

NumPy and SciPy are runtime dependencies. Plotting and pandas tables use the plot and table extras. No R runtime is required.

First example

from ashrink import fit

result = fit(
    [-1.4, -0.6, -0.1, 0.0, 0.2, 0.7, 1.8, 2.2],
    [0.7, 0.4, 0.5, 0.3, 0.5, 0.4, 0.8, 0.7],
    scales=[0.2, 0.6, 1.5],
)
print(result.format_summary())
intervals = result.interval(level=0.95)

The inputs are effect estimates and their standard errors. scales gives fixed normal-prior component standard deviations; the fit estimates their mixture weights. Returned intervals are posterior credible intervals, conditional on those fitted weights. The first three posterior means are [-0.764278, -0.306851, -0.032956].

(a) Input estimates and posterior means. (b) Equal-tailed 95% credible intervals, conditional on the fitted prior. These are fixed illustrative inputs; the plotted values are available as CSV.

Methods

Each method provides posterior estimates, credible intervals, null/sign probabilities, tables and saved results. Plots use the plot extra. See the reference manual (PDF), API guide, worked examples, regression and numerical limits. Reference checks record the installed-R cases and independent analytic checks. Installed-wheel tests pass on macOS and Linux with Python 3.10 and 3.14, and on Windows Server 2025 with Python 3.13. See the tested environments for versions and CI evidence.

Citation

Cite the original statistical method used in an analysis and record the AShrink version or commit. See NOTICE.md for attribution and licenses.