References in zbMATH (referenced in 17 articles )

Showing results 1 to 17 of 17.
Sorted by year (citations)

  1. de Vazelhes, William; Carey, Cj; Tang, Yuan; Vauquier, Nathalie; Bellet, Aurélien: metric-learn: metric learning algorithms in Python (2020)
  2. Leonardo Uieda; Santiago Rubén Soler; Rémi Rampin; Hugo van Kemenade; Matthew Turk; Daniel Shapero; Anderson Banihirwe; John Leeman: Pooch: A friend to fetch your data files (2020) not zbMATH
  3. Andrei V. Novikov: PyClustering: Data Mining Library (2019) not zbMATH
  4. Casper O da Costa-Luis: tqdm: A Fast, Extensible Progress Meter for Python and CLI (2019) not zbMATH
  5. Eric Horton, Chris Parnin: DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets (2019) arXiv
  6. Balaji Sesha Sarath Pokuri; Alec Lofquist; Chad M Risko; Baskar Ganapathysubramanian: PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization (2018) arXiv
  7. Benjamin J. Fulton; Erik A. Petigura; Sarah Blunt; Evan Sinukoff: RadVel: The Radial Velocity Modeling Toolkit (2018) arXiv
  8. Catherine Zucker; Hope How-Huan Chen: RadFil: a Python Package for Building and Fitting Radial Profiles for Interstellar Filaments (2018) arXiv
  9. D.M. Straub: flavio: a Python package for flavour and precision phenomenology in the Standard Model and beyond (2018) arXiv
  10. Eleni Constantinou, Alexandre Decan, Tom Mens: Breaking the borders: an investigation of cross-ecosystem software packages (2018) arXiv
  11. Guedj, Benjamin; Desikan, Bhargav Srinivasa: Pycobra: a Python toolbox for ensemble learning and visualisation (2018)
  12. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  13. Benjamin Guedj, Bhargav Srinivasa Desikan: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation (2017) arXiv
  14. Jarrod R. McClean, Ian D. Kivlichan, Kevin J. Sung, Damian S. Steiger, Yudong Cao, Chengyu Dai, E. Schuyler Fried, Craig Gidney, Brendan Gimby, Pranav Gokhale, Thomas Häner, Tarini Hardikar, Vojtěch Havlíček, Cupjin Huang, Josh Izaac, Zhang Jiang, Xinle Liu, Matthew Neeley, Thomas O’Brien, Isil Ozfidan, Maxwell D. Radin, Jhonathan Romero, Nicholas Rubin, Nicolas P. D. Sawaya, Kanav Setia, Sukin Sim, Mark Steudtner, Qiming Sun, Wei Sun, Fang Zhang, Ryan Babbush: OpenFermion: The Electronic Structure Package for Quantum Computers (2017) arXiv
  15. Berk Ekmekci, Charles E. McAnany, Cameron Mura: An Introduction to Programming for Bioscientists: A Python-based Primer (2016) arXiv
  16. E. E. O. Ishida, S. D. P. Vitenti, M. Penna-Lima, J. Cisewski, R. S. de Souza, A. M. M. Trindade, E. Cameron, V. C. Busti, for the COIN collaboration: cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation (2015) arXiv
  17. Hart, William E.; Watson, Jean-Paul; Woodruff, David L.: Pyomo: modeling and solving mathematical programs in python (2011) ioport