def METHOD_NAME(self):
    """Test function bayesfactor_pearson."""
    # Compare the analytical solution to JASP / R (method='ly')
    # Similar to JASP with kappa=1, or correlationBF with rscale='wide'
    # check for approximate equality with 1e-3 tolerance
    # (as this is how we store the values here)
    assert bfp(0.1, 83) == appr(0.204)
    assert bfp(-0.1, 83) == appr(0.204)
    assert bfp(0.1, 83, alternative="greater") == appr(0.332)
    assert bfp(0.1, 83, alternative="less") == appr(0.076)
    # Example 2. Compare with JASP.
    r, _ = pearsonr(x, y)
    n = 100
    assert bfp(r, n) == appr(0.174)
    assert bfp(r, n, alternative="greater") == appr(0.275)
    assert bfp(r, n, alternative="greater", method="wetzels") == appr(0.275)
    assert bfp(r, n, alternative="less") == appr(0.073)
    r, _ = pearsonr(v, w)
    # relative tolerance here
    assert bfp(r, n) == appr(2.321e22, rel=True)
    assert bfp(r, n, alternative="greater") == appr(4.643e22, rel=True)
    # assert bfp(r, n, alternative='less')) == 1.677e-26
    # Compare the integral solving method (Wetzels)
    # In R:
    # >>> library(BayesFactor)
    # >>> exp(linearReg.R2stat(N=20, p=1, R2=0.36, rscale = 1)[['bf']])
    assert bfp(0.6, 20, method="wetzels") == appr(8.221)
    assert bfp(-0.6, 20, method="wetzels") == appr(8.221)
    assert bfp(0.6, 10, method="wetzels") == appr(1.278)
    # Wrong input
    assert np.isnan(bfp(np.nan, 20))
    assert np.isnan(bfp(0.8, 1))
    assert np.isnan(bfp(np.inf, 1))
    assert np.isinf(bfp(-1, 100))