Comments (2)
Good point. I'll add an option to_pobs
in 0.7.3 which will disable the automatic conversion of the input data in the fit
method. This will be similar to the log_lik
method's signature.
A little background, when I first wrote this for research work, most of the other researchers didn't bother casting the input data to its uniform marginals. Thus the default from the onset was to do this casting automatically (with the exception of the EmpiricalCopula
). Most of the time, I guess it's fine since even if the data was already in its CDF form because if the dataset is representative enough, converting it to its pseudo observations will usually yield the same results. But I can see scenarios where it fails and where a manual treatment of the data is preferred over the automatic conversion.
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Sounds good, thanks!
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Related Issues (20)
- EmpiricalCopula does not work HOT 2
- Error after update HOT 2
- Designation of p_obs does not affect the fit HOT 2
- the summary( ) got converging error HOT 2
- ModuleNotFoundError: No module named 'copulae.special._specfunc' HOT 2
- Python 3.6 not supported? HOT 4
- How to Inverse Normalisation?[Quantile Function] HOT 2
- Fixing Correlation Matrix if copulae.fit() HOT 2
- test_gmc failures HOT 1
- Feature Request - Plot a copula
- Gaussian Mixture Copula Model gives wrong sampling
- Frank and Gaussian Copula give Error
- PEP 517 builds HOT 1
- Ver 0.7.7 - Kendall Tau not supported for Student Copula HOT 1
- Fit Summary HOT 1
- Support for discrete marginals
- Thank you! HOT 2
- An error occurred while importing copulae HOT 2
- GitHub project documentation link is broken HOT 1
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