Statsmodels

statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. The online documentation is hosted at statsmodels.org.


References in zbMATH (referenced in 27 articles )

Showing results 1 to 20 of 27.
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  1. Nikolay Petrov; Vasil Atanasov; Trevor Thompson: Open-Source MUltiple Tests Corrections and FOrmatted Tables Software (MUFOS) (2022) not zbMATH
  2. Tomer Meir, Rom Gutman, Malka Gorfine: PyDTS: A Python Package for Discrete Time Survival Analysis with Competing Risks (2022) arXiv
  3. Yang, Lu; Xie, Naiming; Wei, Baolei; Wang, Xiaolei: On unified framework for nonlinear grey system models: an integro-differential equation perspective (2022)
  4. Yu, Bin; Singh, Chandan: Seven principles for rapid-response data science: lessons learned from COVID-19 forecasting (2022)
  5. Bulavas, Viktoras; Marcinkevičius, Virginijus; Rumiński, Jacek: Study of multi-class classification algorithms’ performance on highly imbalanced network intrusion datasets (2021)
  6. Eshin Jolly: Pymer4: Connecting R and Python for Linear Mixed Modeling (2021) not zbMATH
  7. Estes, Samuel; Dawson, Clint: Uncertainty quantification in reservoirs with faults using a sequential approach (2021)
  8. Julien Siebert, Janek Groß, Christof Schroth: A systematic review of Python packages for time series analysis (2021) arXiv
  9. Schwöbel, Sarah; Marković, Dimitrije; Smolka, Michael N.; Kiebel, Stefan J.: Balancing control: a Bayesian interpretation of habitual and goal-directed behavior (2021)
  10. Hara, Akane; Iwasa, Yoh: Autoimmune diseases initiated by pathogen infection: mathematical modeling (2020)
  11. Hewitt, Mike; Frejinger, Emma: Data-driven optimization model customization (2020)
  12. Mainak Jas; Titipat Achakulvisut; Aid Idrizović; Daniel E. Acuna; Matthew Antalek; Vinicius Marques; Tommy Odland; Ravi Prakash Garg; Mayank Agrawal; Yu Umegaki; Peter Foley; Hugo L Fernandes; Drew Harris; Beibin Li; Olivier Pieters; Scott Otterson; Giovanni De Toni; Chris Rodgers; Eva Dyer; Matti Hamalainen; Konrad Kording; Pavan Ramkumar: Pyglmnet: Python implementation of elastic-net regularized generalized linear models (2020) not zbMATH
  13. Martin Nielsen, Guy Davies, Oliver Hall, et al.: PBjam: A Python package for automating asteroseismology of solar-like oscillators (2020) arXiv
  14. Okuno, Akifumi; Shimodaira, Hidetoshi: Hyperlink regression via Bregman divergence (2020)
  15. Pölsterl, Sebastian: scikit-survival: a library for time-to-event analysis built on top of scikit-learn (2020)
  16. Tavenard, Romain; Faouzi, Johann; Vandewiele, Gilles; Divo, Felix; Androz, Guillaume; Holtz, Chester; Payne, Marie; Yurchak, Roman; Rußwurm, Marc; Kolar, Kushal; Woods, Eli: tslearn, a machine learning toolkit for time series data (2020)
  17. Tomás Capretto, Camen Piho, Ravin Kumar, Jacob Westfall, Tal Yarkoni, Osvaldo A. Martin: Bambi: A simple interface for fitting Bayesian linear models in Python (2020) arXiv
  18. Alex Boyd, Dennis L. Sun: salmon: A Symbolic Linear Regression Package for Python (2019) arXiv
  19. D. Huppenkothen, M. Bachetti, A. L. Stevens, S. Migliari, P. Balm, O. Hammad, U. M. Khan, H. Mishra, H. Rashid, S. Sharma, R. V. Blanco, E. M. Ribeiro: Stingray: A Modern Python Library For Spectral Timing (2019) arXiv
  20. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv

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