Bayesian answers to the questions frequentist tests are usually asked.

bayesplain gives you the Bayesian version of the handful of tests an introductory statistics course is built around — proportions, rates, means, contingency tables, goodness of fit, correlation, a line with one predictor, group comparisons — and prints the conventional test alongside it every time.

It exists because there is a real hole in the Python ecosystem. pingouin gives you Bayes factors for t-tests and correlations; PyMC and Bambi own the modeling end. Nothing covers the middle: contingency tables, proportions, and group comparisons with one consistent interface and output written to be explained rather than pasted into a methods section. This fills that middle, and it is not a port of R's BayesFactor — the positioning difference is the product.

1. Estimation first, Bayes factors second. The headline output is a posterior, a credible interval, and the probability of clearing a threshold you name. BF = 4.2 is not a sentence anyone puts in a memo, so the Bayes factor is a method call (.bayes_factor()) rather than a printed default.

2. Every result carries its frequentist twin. The chi-square statistic, the t, the p-value, the confidence interval — computed and printed next to the posterior, with a plain-English note on what each does and does not claim. You cannot use this package without seeing both numbers for every analysis you run.

3. No sampler in the dependency chain. numpy and scipy, full stop, with matplotlib optional. Everything is a conjugate posterior, a closed-form Bayes factor, or a one-dimensional integral. It installs in a Colab cell in seconds, with no compiler and no convergence warnings, ever. This constraint is also the scope police: anything that would need MCMC is out of scope by construction, and the docs hand you off to Bambi at that boundary.

pip install bayesplain          # numpy + scipy only
pip install bayesplain[plot]    # adds matplotlib

Two districts' eviction filing rates. The Bayesian answer is actionable; the frequentist answer is "not significant"; both come from the same four numbers.

import bayesplain as bp

res = bp.compare_proportions(
    successes=[34, 51],
    n=[220, 240],
    labels=["District A", "District B"],
)
print(res.summary())

==========================================================================
 difference in rate (District B − District A)
==========================================================================

 BAYESIAN — what the data say about the quantity itself

   best estimate (median)    5.7 percentage points
   95% credible interval     −1.2 to 12.9 percentage points  (HDI)
   P(District B higher)      0.945

   Read: District B is higher than District A with 94% probability; the
         best estimate of the gap is 5.7 percentage points, and the data
         are consistent with anything from −1.2 to 12.9 percentage points
         (95% credible interval).

 FREQUENTIST — two-proportion z-test (equivalently chi-square, 1 df)

   chi-square (1 df)         2.559
   p-value                   0.1096  (not significant at 0.05)
   95% confidence interval   −1.2 to 12.8 percentage points  (Wald)

   Read: If the two rates were identical, data at least this extreme would
         turn up 11.0% of the time.

   --------------------------------------------------------------------

   Why they look like they disagree
     They are answering different questions. The test asks how surprising
     this data would be in a world where the two rates were identical,
     and 11.0% is not surprising enough to clear the conventional bar.
     The posterior asks how plausible each possible difference is given
     the data you have, and puts 94% of that plausibility on one side.
     Neither is wrong. Only the second one is an input to a decision.

--------------------------------------------------------------------------
 assumption   prior: uninformed — Beta(1, 1)
 precision    100,000 independent draws; Monte Carlo error on the mean
              is ±0.00011. These are exact samples, not a Markov chain.
 next         .probability('>', x)  .decide(rope=(lo, hi))  .sensitivity()
==========================================================================

Note the two intervals nearly coincide. The numbers agree; only the sentences differ. That is the lesson, not a coincidence to explain away.

Every analysis returns the same object with the same methods, so what you learn in week 2 still works in week 9.

res.probability(">", 0.05)     # P(the gap exceeds 5 points)
res.interval(0.95, kind="hdi") # or kind="eti" to line up with a CI
res.decide(rope=(-0.02, 0.02)) # HDI + ROPE, in place of "reject the null"
res.sentence()                 # one memo-ready line
res.translate()                # what the p-value claims vs the posterior
res.sensitivity()              # how the conclusion moves as the prior widens
res.predict(n=100)             # forecast the next 100 cases, uncertainty included
res.plot(kind="components")    # where the difference came from
res.plot_kinds()               # what this particular result can draw
res.to_dict()                  # autograding hook
res.draws                      # ndarray, so you can compute anything
res.bayes_factor()             # available, documented, never the headline

.sensitivity() is a first-class method, not an advanced footnote. Prior sensitivity is the strongest legitimate objection to canned Bayesian analysis, and reporting it by default converts that objection into a scheduled lecture. On the example above it also surfaces something worth teaching: the credible interval barely moves across every prior in the ladder, while the Bayes factor moves by a factor of 19. That asymmetry is the reason this package leads with the estimate.

Nobody should have to type a shape parameter to get started, and nobody should be able to hide which assumption they used.

bp.priors.describe()                                  # what each preset assumes
bp.proportion(34, 220, prior="skeptical")             # named preset
bp.proportion(34, 220, prior=(2, 5))                  # explicit (a, b)
bp.priors.from_previous_study(successes=12, n=80)     # an earlier study as a prior

The resolved prior and its parameters print in every summary.

The default seed is fixed (bp.get_seed() → 12345). That is unusual for a research package and correct for a teaching one: if every student gets different digits in the last decimal place, office hours fill with questions about Monte Carlo noise instead of questions about statistics. Having hidden that noise, the package then surfaces it — every sampled quantity prints its Monte Carlo standard error. Use bp.set_seed(None) to demonstrate that the wobble is real.

All built, tested, and documented.

proportion, rate, mean, and regression also forecast the next observation with .predict(), next to the plug-in forecast that ignores the uncertainty in the estimate.

Explicitly out of scope: multi-factor ANOVA, interactions, regression with more than one predictor, overdispersed counts, mixed models, anything needing importance sampling over multiple hyperparameters. For those, go to Bambi — the docs include a worked handoff. There is also deliberately no omnibus Bayes factor for compare_groups; .pairwise() reports a validated one per pair instead, and the method explains why when you ask for the omnibus version.

Four small, real, pre-cleaned planning datasets ship inside the package, so no problem set dies because a city reorganised its open-data portal.

bp.datasets.describe()             # one line each
bp.datasets.load_collisions()      # LA and San Diego collisions, 2019-2023
bp.datasets.load_tracts()          # LA County ACS tracts
bp.datasets.load_blockgroups()     # the same variables, finer geography
bp.datasets.load_parcels()         # Baltimore residential parcels

The tract/block-group pair exists for one purpose: identical variables, identical county, two aggregations, and non-overlapping credible intervals for the same correlation (0.781 vs 0.695). The modifiable areal unit problem, with the arithmetic to show it is not sampling noise.

Nothing is asserted on the strength of having been transcribed carefully.

Published reference values. The contingency Bayes factors reproduce the worked examples in Jamil et al. (2017) under all four Gunel-Dickey schemes: log BF₁₀ = 23.0337 against a published 23.03 for the doll-preference study, BF₁₀ = 9.194 against 9.19 (Poisson), 3.043 against 3.04 (hypergeometric), and the posterior log odds ratio at 2.47 with a 95% interval of (1.73, 3.26) against a published (1.73, 3.26).

Independent implementations. The JZS t-test Bayes factor matches pingouin.bayesfactor_ttest to six significant figures across one-sample, two-sample, and every prior width; the correlation Bayes factor matches pingouin.bayesfactor_pearson to seven. pingouin is a validation-only dependency, never required at runtime.

Self-validation. Marginal likelihoods are checked against brute-force quadrature of the same integral and against summing to 1 over all outcomes. The exact correlation sampling density is checked against its own normalisation over (−1, 1), which pins the hypergeometric transcription without reference to anyone's code.

Behavioural properties. Posterior means fall between the prior mean and the MLE; interval widths shrink as 1/√n; wider priors weaken the Bayes factor for a small effect; swapping group order flips the sign and nothing else; results are invariant to relabelling categories.

Pedagogical regression tests (marked pedagogy). The numbers a lecture depends on are pinned as assertions. If a refactor breaks a lecture, CI says so rather than a student.

Two notes on the contingency Bayes factor, since the earlier version of this README flagged it as unverified. It is now validated against the published values above. It is computed in a form that differs algebraically from Jamil et al.'s Equation 10 — each row drawing its own column distribution rather than their successive conditioning — and the two agree exactly once expanded in gamma functions; the advantage of this form is that it accepts a per-column concentration vector, so a Beta(a, b) prior on a rate carries into the Bayes factor unchanged. Separately, bayesplain evaluates the correlation marginal likelihood in log space, which means it returns a finite answer where pingouin returns inf for strongly correlated large samples — including the ~2,400-tract case in the bundled data.

pip install -e ".[docs]"
cd docs && make html

Sphinx with sphinx-immaterial and executed notebooks, in the same shape as bayesplain. Builds clean with no warnings.

pip install -e ".[tests,plot,data,validate]"
pytest                  # fast suite, including doctests
pytest -m ''            # add the slow Monte Carlo validation runs
pytest -m validation    # only the cross-implementation checks
pytest -m pedagogy      # only the numbers a lecture depends on
ruff check . && ruff format --check .

BSD 3-Clause.