12. Bibliografía#
12.1. Librerías#
ArviZ [Kumar et al., 2019]
Bambi [Capretto et al., 2022]
Lifelines [Davidson-Pilon, 2019]
Matplotlib [Hunter, 2007]
NumPy [Harris et al., 2020]
Pandas [The pandas development team, 2025]
Pingouin [Vallat, 2018]
SciPy [Virtanen et al., 2020]
scikit‑learn [Pedregosa et al., 2011]
seaborn [Waskom, 2021]
statsmodels [Seabold and Perktold, 2010]
12.2. Referencias Bibliográficas#
Aquí se enlistan todas las referencias del libro.
Hadley Wickham. Tidy data. The Journal of Statistical Software, 2014. URL: http://www.jstatsoft.org/v59/i10/.
Allison Marie Horst, Alison Presmanes Hill, and Kristen B Gorman. palmerpenguins: Palmer Archipelago (Antarctica) penguin data. 2020. R package version 0.1.0. URL: https://allisonhorst.github.io/palmerpenguins/, doi:10.5281/zenodo.3960218.
Ravin Kumar, Colin Carroll, Ari Hartikainen, and Osvaldo Martin. Arviz a unified library for exploratory analysis of bayesian models in python. Journal of Open Source Software, 4(33):1143, 2019. doi:10.21105/joss.01143.
Tomás Capretto, Camen Piho, Ravin Kumar, Jacob Westfall, Tal Yarkoni, and Osvaldo A. Martin. Bambi: a simple interface for fitting bayesian linear models in python. Journal of Statistical Software, 103(15):1–29, 2022. doi:10.18637/jss.v103.i15.
Cameron Davidson-Pilon. Lifelines: survival analysis in python. Journal of Open Source Software, 4(40):1317, 2019. URL: https://doi.org/10.21105/joss.01317, doi:10.21105/joss.01317.
J. D. Hunter. Matplotlib: a 2d graphics environment. Computing in Science & Engineering, 9(3):90–95, 2007. doi:10.1109/MCSE.2007.55.
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, ..., and T. E. Oliphant. Array programming with numpy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.
The pandas development team. pandas-dev/pandas: Pandas. 2025. URL: pandas-dev/pandas, doi:10.5281/zenodo.3509134.
Raphael Vallat. Pingouin: statistics in python. Journal of Open Source Software, 3(31):1026, November 2018. doi:10.21105/joss.01026.
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Mat Haberland, Tyler Reddy, David Cournapeau, and ... SciPy 1.0 Contributors. Scipy 1.0: fundamental algorithms for scientific computing in python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, and E. ... Duchesnay. Scikit-learn: machine learning in python. Journal of Machine Learning Research, 12:2825–2830, 2011.
M. L. Waskom. Seaborn: statistical data visualization. Journal of Open Source Software, 6(60):3021, 2021. doi:10.21105/joss.03021.
Skipper Seabold and Josef Perktold. Statsmodels: econometric and statistical modeling with python. In 9th Python in Science Conference. 2010.