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Description
🚀 Feature Request
The result would be a dataframe, something like:
| est | lower | upper | description | |
|---|---|---|---|---|
| kelley | 57.774 | 41.287 | 97.894 | cv with Kelley 95% CI |
| mckay | 57.774 | 41.441 | 108.483 | cv with McKay 95% CI |
| miller | 57.774 | 34.053 | 81.495 | cv with Miller 95% CI |
| vangel | 57.774 | 41.264 | 105.426 | cv with Vangel 95% CI |
| mahmoudvand_hassani | 57.774 | 43.476 | 82.857 | cv with Mahmoudvand-Hassani 95% CI |
| equal_tailed | 57.774 | 43.937 | 84.383 | cv with Equal-Tailed 95% CI |
| shortest_length | 57.774 | 42.015 | 81.013 | cv with Shortest-Length 95% CI |
| normal_approximation | 57.774 | 44.533 | 85.272 | cv with Normal Approximation 95% CI |
| norm | 57.774 | 38.799 | 78.937 | cv with Normal Approximation Bootstrap 95% CI |
| basic | 57.774 | 35.055 | 78.167 | cv with Basic Bootstrap 95% CI |
| perc | 57.774 | 38.879 | 79.174 | cv with Bootstrap Percentile 95% CI |
| bca | 57.774 | 40.807 | 82.297 | cv with Adjusted Bootstrap Percentile (BCa) 95% CI |
🔈 Motivation
There are various methods for the calculation of confidence intervals (CI) for cv. All of them are fruitful and have particular use cases. Some of them are model-based hence their usage depends on the assumptions regarding the distribution of data. For sake of versatility, cover almost all of these methods.