Inverse / normal-inverse gaussian distributions #954
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| assert!(InverseGaussian::new(-1.0, -1.0).is_err()); | ||
| assert!(InverseGaussian::new(1.0, -1.0).is_err()); | ||
| assert!(InverseGaussian::new(1.0, 1.0).is_ok()); | ||
| } |
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It would be nice to have a test for value stability, but I guess that can also be added later.
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I added a value stability test by trying to reproduce what was done for the other distributions but I'm not sure of what is to be tested
| assert!(NormalInverseGaussian::new(-1.0, -1.0).is_err()); | ||
| assert!(NormalInverseGaussian::new(1.0, 2.0).is_err()); | ||
| assert!(NormalInverseGaussian::new(2.0, 1.0).is_ok()); | ||
| } |
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Again, a value stability test could be added.
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I did not verify whether the sampling is correct, but the code looks good. |
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@vks I checked the sampling of the normal-inverse Gaussian distribution manually by generating a bunch of points, fitted them using scipy's norminvgauss function and checked that the parameters were the same. |
Discussed in #952 .