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 22 articles )

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  1. Eshin Jolly: Pymer4: Connecting R and Python for Linear Mixed Modeling (2021) not zbMATH
  2. Estes, Samuel; Dawson, Clint: Uncertainty quantification in reservoirs with faults using a sequential approach (2021)
  3. Julien Siebert, Janek Groß, Christof Schroth: A systematic review of Python packages for time series analysis (2021) arXiv
  4. Schwöbel, Sarah; Marković, Dimitrije; Smolka, Michael N.; Kiebel, Stefan J.: Balancing control: a Bayesian interpretation of habitual and goal-directed behavior (2021)
  5. Hara, Akane; Iwasa, Yoh: Autoimmune diseases initiated by pathogen infection: mathematical modeling (2020)
  6. Hewitt, Mike; Frejinger, Emma: Data-driven optimization model customization (2020)
  7. 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
  8. Martin Nielsen, Guy Davies, Oliver Hall, et al.: PBjam: A Python package for automating asteroseismology of solar-like oscillators (2020) arXiv
  9. Okuno, Akifumi; Shimodaira, Hidetoshi: Hyperlink regression via Bregman divergence (2020)
  10. Pölsterl, Sebastian: scikit-survival: a library for time-to-event analysis built on top of scikit-learn (2020)
  11. 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)
  12. 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
  13. Alex Boyd, Dennis L. Sun: salmon: A Symbolic Linear Regression Package for Python (2019) arXiv
  14. 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
  15. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  16. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  17. Raphael Saavedra, Guilherme Bodin, Mario Souto: StateSpaceModels.jl: a Julia Package for Time-Series Analysis in a State-Space Framework (2019) arXiv
  18. Tanaka, Emi; Hui, Francis K. C.: Symbolic formulae for linear mixed models (2019)
  19. Bijlsma, Tjerk; Lint, Alexander; Verriet, Jacques: Early design phase cross-platform throughput prediction for industrial stream-processing applications (2018)
  20. Gordon, Steven I.; Guilfoos, Brian: Introduction to modeling and simulation with MATLAB and Python (2017)

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