hyppo: A Multivariate Hypothesis Testing Python Package
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914948159897600 |
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| author | Panda, Sambit Palaniappan, Satish Xiong, Junhao Bridgeford, Eric W. Mehta, Ronak Shen, Cencheng Vogelstein, Joshua T. |
| author_facet | Panda, Sambit Palaniappan, Satish Xiong, Junhao Bridgeford, Eric W. Mehta, Ronak Shen, Cencheng Vogelstein, Joshua T. |
| contents | We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. hyppo includes many state of the art multivariate testing procedures. The package is easy-to-use and is flexible enough to enable future extensions. The documentation and all releases are available at https://hyppo.neurodata.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1907_02088 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | hyppo: A Multivariate Hypothesis Testing Python Package Panda, Sambit Palaniappan, Satish Xiong, Junhao Bridgeford, Eric W. Mehta, Ronak Shen, Cencheng Vogelstein, Joshua T. Computation Mathematical Software Methodology Machine Learning We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. hyppo includes many state of the art multivariate testing procedures. The package is easy-to-use and is flexible enough to enable future extensions. The documentation and all releases are available at https://hyppo.neurodata.io. |
| title | hyppo: A Multivariate Hypothesis Testing Python Package |
| topic | Computation Mathematical Software Methodology Machine Learning |
| url | https://arxiv.org/abs/1907.02088 |