VsusP

VsusP selects variables from shrinkage-prior posterior coefficient draws. It provides sequential two-means (S2M), basic two-means (2-M), spike-and-slab inclusion summaries, the signal adaptive variable selector (SAVS), and an adaptive-L1 approximation to decoupling shrinkage and selection (DSS). Native Gaussian ridge, horseshoe and spike-and-slab fits provide coefficient draws in Python.

Install

pip install vsusp

From GitHub:

python -m pip install "git+https://github.com/nilson01/vsusp-python.git"

Or from a local source directory:

python -m pip install .

For plots and sampling diagnostics:

pip install "vsusp[plots,diagnostics]"

Select variables

Rows contain posterior draws; columns contain coefficients. Exclude the intercept.

import numpy as np
from vsusp import s2m

beta = np.array([[.01, .02, .03, .3, .31, 10, 11],
                 [-.01, -.02, -.03, -.3, -.31, -10, -11]])
path = s2m(beta, [.1, .5])
print(path.summary())
print(path.select(1).selected_names)
# ('x7', 'x6')

Indices are zero-based. The first threshold has index 0. The path shows the selected count at each threshold; it does not choose an optimal threshold.

Choose an analysis

The documentation website includes the tutorials cover fitting, prediction and spike-and-slab draws. The API reference lists inputs and outputs. The reference manual collects the examples, methods and API.

Interpretation

S2M counts and ranking summaries are not posterior inclusion probabilities. Only exact-zero spike-and-slab draws support spike_slab inclusion frequencies. SAVS and DSS can return an empty selection. Their sparse coefficients depend on the stated design and penalty conventions.

Python uses deterministic exact two-means clustering by default. The R package uses randomized k-means by default. The optional Python kmeans backend uses NumPy Lloyd iterations and does not reproduce R's random-number stream.

Assess chain mixing and tuning sensitivity before interpreting fitted results. Short examples and numerical agreement do not establish false-discovery control or general selection accuracy. See methods and limits.

References

Li, H. and Pati, D. (2017). Variable selection using shrinkage priors. Computational Statistics & Data Analysis, 107, 107–119.

Ray, P. and Bhattacharya, A. (2018). Signal Adaptive Variable Selector for the Horseshoe Prior.

Hahn, P. R. and Carvalho, C. M. (2015). Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective. JASA, 110, 435–448.

Numerical evidence: fixed posterior checks.

License and source attribution: NOTICE.

Acknowledgements

Computations were performed on the Arseven Computing Cluster, Department of Statistics, Texas A&M University.

This work received no grant funding.