García-Maroto Editores

ECONOMETRÍA

CON APLICACIONES EN R Y PYTHON

AUTOR: JULIÁN RAMAJO HERNÁNDEZ, EDITORIAL: GARCÍA-MAROTO EDITORES

https://www.ingebook.com/ib/NPcd/IB_BooksVis?cod_primaria=1000187&codigo_libro=17832

Para la ejecución de la aplicaciones econométricas presentadas en el libro se usan los lenguajes de programación R (https://www.r-project.org/) y Python (https://www.python.org), utilizando como entorno de desarrollo integrado -IDE- de dichos lenguajes el software RStudio (https://posit.co/products/open-source/rstudio/). Todos los scripts del libro funcionan igualmente con la nueva IDE de Posit PBC, Positron (https://positron.posit.co/).

Algunos de los ejemplos del libro se pueden ejecutar en modo interactivo utilizando la página web siguiente: https://jramajo.quarto.pub/ecmtrpy/.

Los alumnos de la Universidad de Extremadura pueden trabajar online con R y Python a través de RStudio Server, instalado en el servidor SAGE: https://sage.unex.es/rstudio/.

Recursos de software:

RStudio, Positron, Quarto, Reticulate, R y Python

RStudio IDE: https://posit.co/products/open-source/rstudio/
- Posit Cheatsheets: https://rstudio.github.io/cheatsheets/
- Posit Blog: https://posit.co/blog/
Positron IDE: https://positron.posit.co/
Quarto: https://quarto.org/
- Quarto Cheatsheet: https://rstudio.github.io/cheatsheets/html/quarto.html
- Quarto Blog: https://quarto.org/docs/blog/
Reticulate: https://rstudio.github.io/reticulate/ (video)
- Reticulate Cheatsheet: https://rstudio.github.io/cheatsheets/html/reticulate.html
R Project: https://www.r-project.org/ (https://cloud.r-project.org/)
- RStudio Education: https://education.rstudio.com/learn/
- R-bloggers: https://www.r-bloggers.com/
- R Manuals: https://rstudio.github.io/r-manuals/
- R Package Documentation: https://rdrr.io/
- R Packages: https://cloud.r-project.org/web/packages/index.html
  - Task Views: https://cloud.r-project.org/web/views/
Python Project: https://www.python.org/
- Learn Python: https://www.learnpython.org/es/
- Python-bloggers: https://python-bloggers.com/
- Python Documentation: https://www.python.org/doc/
- PyPI - Python Package Index: https://pypi.org/

Programación, manejo y visualización de datos con R y/o Python. Librerías básicas

R

Big Book of R: https://www.bigbookofr.com/
R para principiantes: https://bookdown.org/jboscomendoza/r-principiantes4/
Introducción a R: https://oscarperpinan.github.io/R/
Programación práctica con R: https://davidrsch.github.io/hopres/
Introduction to Programming with R: https://discdown.org/rprogramming/
Modern R with the tidyverse: https://modern-rstats.eu/
Introduction to R for Econometrics: https://bookdown.org/kieranmarray/intro_to_r_for_econometrics/
R for Data Science (2e): https://r4ds.hadley.nz/ [ESP: https://davidrsch.github.io/r4dses/]
Advanced R: https://adv-r.hadley.nz/ [ESP: https://davidrsch.github.io/adv-res/]
Hand-On Programming with R: https://rstudio-education.github.io/hopr/ [ESP: https://davidrsch.github.io/hopres/]
R in Action: https://github.com/rkabacoff/RiA3
Statistical Inference via Data Science: https://moderndive.com/v2/
Introduction to Data Science: https://rafalab.dfci.harvard.edu/dsbook-part-1/ [ESP: https://rafalab.github.io/dslibro/]
Tidy Modeling with R: https://www.tmwr.org/ [ESP: https://davidrsch.github.io/TMwRes/]
Tidy Finance with R: https://tidy-finance.org/
R for Everyone: https://www.jaredlander.com/r-for-everyone/
Efficient R Programming: https://csgillespie.github.io/efficientR/
R Cookbook: https://rc2e.com/
Cookbook for R: http://www.cookbook-r.com/
A First Course in Statistical Programming with R: http://www.murdoch-sutherland.com/StatProg3/
R Programming for Data Science: https://bookdown.org/rdpeng/rprogdatascience/
Fundamentals of Data Visualization: https://clauswilke.com/dataviz/
Data Visualization: https://socviz.co/
ggplot2: Elegant Graphics for Data Analysis: https://ggplot2-book.org/
Data Visualization with R: https://rkabacoff.github.io/datavis/
R Graphics Cookbook: https://r-graphics.org/
R Graph Gallery: https://r-graph-gallery.com/
From Data to Viz: https://www.data-to-viz.com/
Interactive web-based data visualization with R, plotly and shiny: https://plotly-r.com/
Mastering shiny: https://mastering-shiny.org/
Engineering Production-Grade Shiny Apps: https://engineering-shiny.org/
R Markdown (The Definitive Guide): https://bookdown.org/yihui/rmarkdown/
R Markdown Cookbook: https://bookdown.org/yihui/rmarkdown-cookbook/
bookdown: Authoring Books and Technical Documents with R Markdown: https://bookdown.org/yihui/bookdown/
blogdown: Creating Websites with R Markdown: https://bookdown.org/yihui/blogdown/
Reproducible Research with R and RStudio: https://github.com/christophergandrud/Rep-Res-Book
Data Analysis with RStudio: http://www.statistik-kronthaler.ch/?page_id=376
Learn R as a Language: https://www.taylorfrancis.com/books/9780367182533
Deep R Programming: https://deepr.gagolewski.com/
PsyTeachR: https://psyteachr.github.io/

Librerías:

Tidyverse: https://www.tidyverse.org/ [TidyDataTutor] [TidyExplain]
Tidyverts: https://tidyverts.org/
Tidymodels: https://www.tidymodels.org/
Plotly-R: https://plotly.com/r/
Leaflet: https://rstudio.github.io/leaflet/
DataEditR: https://dillonhammill.github.io/DataEditR/index.html
Esquisse: https://dreamrs.github.io/esquisse/index.html
Radiant: https://radiant-rstats.github.io/docs/
Easystats: https://easystats.github.io/easystats/
Econometrics (CRAN): https://cran.r-project.org/web/views/Econometrics.html
TimeSeries (CRAN): https://cran.r-project.org/web/views/TimeSeries.html
Spatial (CRAN): https://cran.r-project.org/web/views/Spatial.html
SpatioTemporal (CRAN): https://cran.r-project.org/web/views/SpatioTemporal.html
Finance (CRAN): https://cran.r-project.org/web/views/Finance.html

PYTHON

Big Book of Python: https://www.bigbookofpython.com/
Introducción a la Programación con Python: https://plataforma.josedomingo.org/pledin/cursos/programacion_python3/index.html
Introduction to Python (Workshop): https://monashdatafluency.github.io/python-workshop-base/
Python for Data Analysis (3e): https://wesmckinney.com/book/ [ESP: https://archive.org/details/python-para-analisis-de-datos]
Python for Data Science: https://aeturrell.github.io/python4DS/
Python Programming for Data Science: https://www.tomasbeuzen.com/python-programming-for-data-science/
Python Data Science Handbook: https://jakevdp.github.io/PythonDataScienceHandbook/
Think Python: https://allendowney.github.io/ThinkPython/
The Python Book: https://www.wiley.com/en-us/The+Python+Book-p-9781119573289
Python for Everyone: https://horstmann.com/python4everyone/
Intro to Python for Econometrics, Statistics and Numerical Analysis: https://www.kevinsheppard.com/teaching/python/notes/
Intro to Python for Computer Science and Data Science: https://deitel.com/intro-to-python-for-computer-science-and-data-science/
Foundational Python for Data Science: https://www.pearson.com/en-us/subject-catalog/p/foundational-python-for-data-science/P200000007643/9780137605675
Python Cookbook: https://www.oreilly.com/library/view/python-cookbook-3rd/9781449357337/
The Quick Python Book: https://www.manning.com/books/the-quick-python-book-third-edition
Python Programming for Data Analysis: https://www.springer.com/gp/book/9783030689513
Introduction to Computation and Programming Using Python: https://mitpress.mit.edu/books/introduction-computation-and-programming-using-python-third-edition
Minimalist Data Wrangling with Python: https://datawranglingpy.gagolewski.com/

Librerías:

Numpy: https://numpy.org/
Pandas: https://pandas.pydata.org/ [PandasTutor]
Matplotlib: https://matplotlib.org/
Seaborn: https://seaborn.pydata.org/
Siuba (dplyr en Python): https://github.com/machow/siuba
Plotnine (ggplot en Python): https://plotnine.readthedocs.io/en/stable/
Plotly-Python: https://plotly.com/python/
Vega-Lite: https://vega.github.io/vega-lite/ ; Vega-Altair: https://altair-viz.github.io/index.html
Bokeh: https://bokeh.org/
Statmodels: https://www.statsmodels.org/
Linearmodels: https://bashtage.github.io/linearmodels/
SciPy: https://scipy.org/
Scikit-learn: https://scikit-learn.org/
Geopandas: https://geopandas.org/
PySAL: http://pysal.org/

RECURSOS BibliogrÁfICOS:

Básicos

Matilla, M. et al. (2017): Econometría y Predicción -> https://www.ingebook.com/ib/NPcd/IB_BooksVis?cod_primaria=1000187&codigo_libro=15046
Stock, J. y Watson, M. (2012): Introducción a la Econometría -> https://www.ingebook.com/ib/NPcd/IB_BooksVis?cod_primaria=1000187&codigo_libro=6564
Wickham, H. et al. (2023): R for Data Science (2e) -> https://r4ds.hadley.nz/ [ES: https://davidrsch.github.io/r4dses/]
McKinney, W. (2023): Python for Data Analysis (3e) -> https://wesmckinney.com/book/
Timbers, T. et al. (2024-25): Data Science (with R and Python) --> https://datasciencebook.ca/ - https://python.datasciencebook.ca/
Clark, M. y Berry, S. (2026): Models Demystified (with R and Python) -> https://m-clark.github.io/book-of-models/

Complementarios

Asteriou, D. y Hall, S.G. (2021): Applied Econometrics, MACMILLAN.
Bailey, M: (2020): Real Econometrics, OXFORD UNIVERSITY PRESS.
- Carilli, T. (2021): R companion to Real Econometrics -> https://bookdown.org/carillitony/bailey/
Békés, G. y Kézdi, G. (2021): Data Analysis for Business, Economics and Policy, CAMBRIDGE UNIVERSITY PRESS.
- Gabors Data Analysis (Web site): https://gabors-data-analysis.com/
- Gabors Data Analysis (GitHub): https://github.com/gabors-data-analysis
Brooks, C. (2019): Introductory Econometrics for Finance, CAMBRIDGE UNIVERSITY PRESS.
- Wichmann, R. y Brooks, C. (2019): R Guide to Accompany 'Introductory Econometrics for Finance' -> https://www.cambridge.org/download_file/1004241
- Tao, R. y Brooks, C. (2019): Python Guide to Accompany 'Introductory Econometrics for Finance' -> https://www.cambridge.org/download_file/1004256
Chan, J. et al. (2019): Bayesian Econometric Methods, CAMBRIDGE UNIVERSITY PRESS.
Chatterjee, S. y Simonoff, J.S. (2020): Handbook of Regression Analysis with Applications in R, WILEY -> https://pages.stern.nyu.edu/~jsimonof/RegressionHandbook/
Enders, W. (2015): Applied Econometric Time Series, WILEY.
Fahrmeir, L. et al. (2021): Regression - Models, Methods and Applications, SPRINGER.
Fox, J. (2016): Applied Regression Analysis and Generalized Linear Models, SAGE.
- Web site: https://www.john-fox.ca/AppliedRegression/index.html
Garson, G.D. (2022): Data Analytics for the Social Sciences - Applications in R, ROUTLEDGE. -> https://routledgetextbooks.com/textbooks/9780367624279/
Gelman A., Hill J. y Vehtari, A. (2021): Regression and Other Stories, CAMBRIDGE UNIVERSITY PRESS.
Greene, W.H. (2020): Econometric Analysis, PEARSON.
Hansen, B. (2022): Econometrics, PRINCETON UNIVERSITY PRESS.
Hilmer, C.E. y Hilmer, M.J. (2014): Practical Econometrics, MCGRAW-HILL EDUCATION.
Hill R.C., Griffiths W.E y Lim G.C. (2018): Principles of Econometrics, WILEY.
- Colonescu, C. (2016): Principles of Econometrics with R -> https://bookdown.org/ccolonescu/RPoE4/
- Computing Supplements: http://www.principlesofeconometrics.com/poe5/poe5computing.html
James, G. et al. (2023): An Introduction to Statistical Learning, SPRINGER -> https://www.statlearning.com/
- Hvitfeldt, E. (2023): ISLR tidymodels labs -> https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/
Koop, G. (2003): Bayesian Econometrics, WILEY.
Mignon, V. (2024): Principles of Econometrics: Theory and Applications, SPRINGER.
Stock J.H. y Watson M.W. (2020): Introduction to Econometrics, PEARSON.
- Hanck, C. et al. (2024): Introduction to Econometrics with R -> https://www.econometrics-with-r.org/
Verbeek, M. (2017): A Guide to Modern Econometrics, WILEY.
Wooldridge, J.M. (2020): Introductory Econometrics, CENGAGE.
- Heiss, F. and Brunner, D. (2020): Using R, Python (and Julia) for Introductory Econometrics -> http://www.urfie.net/

Análisis estadístico, econométrico y ciencia de datos con R y/o Python

R

Adams, C.P. (2021): Learning Microeconometrics with R -> https://sites.google.com/view/microeconometricswithr/
Agresti, A. (2019): An Introduction to Categorical Data Analysis -> https://bcs.wiley.com/he-bcs/Books?action=index&bcsId=11286&itemId=1119405262
Albert, J. (2009): Bayesian Computation with R -> https://bayesball.github.io/bcwr/
Alexander, R. (2023): Telling Stories with Data - With Applications in R -> https://tellingstorieswithdata.com/
Ang, C.S. (2021): Analyzing Financial Data and Implementing Financial Models Using R -> http://cliffordang.com/
Arbia, G. (2014): A Primer for Spatial Econometrics with Applications in R -> https://www.palgrave.com/gp/book/9780230360389
Barrett, M. et al. (2024): Causal Inference in R -> https://www.r-causal.org/
Baumer, B.S. et al. (2023): Modern Data Science with R -> https://mdsr-book.github.io/mdsr2e/
Berk, R.A. (2020): Statistical Learning from a Regression Perspective -> https://link.springer.com/book/10.1007/978-3-319-44048-4
Bischl, B et al. (Eds.) (2024): Applied Machine Learning Using mlr3 in R -> https://mlr3book.mlr-org.com/
Bivand, R. et al. (2013): Applied Spatial Data Analysis with R -> https://asdar-book.org/
Boehmke, B. y Greenwell, B. (2020): Hands-On Machine Learning with R -> https://bradleyboehmke.github.io/HOML/
Bonnell, J. y Ogihara, M. (2024): Exploring Data Science with R and the Tidyverse -> https://ds4world.cs.miami.edu/
Bougioukas, K.I. (2024): Practical Statistics in Medicine with R -> https://practical-stats-med-r.netlify.app/
Brunsdon, C. y Comber, L. (2019): An Introduction to R for Spatial Analysis and Mapping -> https://study.sagepub.com/brunsdon2e
Brus, D.J. (2023): Spatial Sampling with R -> https://dickbrus.github.io/SpatialSamplingwithR/
Çetinkaya-Rundel, M. y Hardin, J. (2023): Introduction to Modern Statistics -> https://openintro-ims2.netlify.app/
Chapman, C. y Feit E.M. (2019): R for Marketing Research and Analytics -> http://r-marketing.r-forge.r-project.org/
Chatfield C. y Xing H. (2019): The Analysis of Time Series - An Introduction with R -> http://www.ams.sunysb.edu/~xing/tsRbook/
Chen, D.G. y Chen, J.K. (2021): Statistical Regression Modeling with R - Longitudinal and Multi-Level Modeling -> https://link.springer.com/book/10.1007%2F978-3-030-67583-7
Comber, L. y Brunsdon, C. (2021): Geographical Data Science and Spatial Data Analysis -> https://study.sagepub.com/comber
Congdon, P. (2020): Bayesian Hierarchical Models - With Applications Using R -> https://www.qmul.ac.uk/geog/staff/congdonp.html
Crawley, M.J. (2015): Statistics - An Introduction using R -> http://www.bio.ic.ac.uk/research/crawley/statistics/
Croissant, Y. y Millo, G. (2019): Panel Data Econometrics with R -> https://bcs.wiley.com/he-bcs/Books?action=index&bcsId=11272&itemId=1118949161
Dayal, V. (2020): Quantitative Economics with R - A Data Science Approach -> https://link.springer.com/book/10.1007%2F978-981-15-2035-8
Denis, D.J. (2020): Applied Univariate, Bivariate, and Multivariate Statistics -> http://www.datapsyc.com/
Fahrmeir, L. et al. (2021): Regression - Models, Methods and Applications -> https://www.uni-goettingen.de/de/550514.html
Faraway, J. (2015): Linear Models with R -> https://julianfaraway.github.io/faraway/LMR/
Faraway, J. (2016): Extending the Linear Model with R -> https://julianfaraway.github.io/faraway/ELM/
Fernández-Avilés, G. y Montero, J.M., Eds. (2024): Fundamentos de ciencia de datos con R -> https://cdr-book.github.io/
Fox, J. (2016): Applied Regression Analysis and Generalized Linear Models -> https://socialsciences.mcmaster.ca/jfox/Books/Applied-Regression-3E/index.html
Fox, J. (2020): Regression Diagnostics -> https://socialsciences.mcmaster.ca/jfox/Books/RegressionDiagnostics/
French, T. (2022): R for Data Analysis -> https://trevorfrench.github.io/R-for-Data-Analysis/
Friendly, M. y Meyer, D. (2016): Discrete Data Analysis with R -> http://ddar.datavis.ca/
Gentle, J.E. (2020): Statistical Analysis of Financial Data with Examples in R -> https://mason.gmu.edu/~jgentle/books/StatFinBk/
Gimond, M. (2023): Intro to GIS and Spatial Analysis -> https://mgimond.github.io/Spatial/
Grolemund, G. (2022): Hands-On Programming with R -> https://jjallaire.github.io/hopr/
Heiberger, R.M. y Holland, B. (2015): Statistical Analysis and Data Display -> https://www.springer.com/us/book/9781493921218
Henderson, D.J. y Parmeter, D. (2015): Applied Nonparametric Econometrics -> https://www.the-smooth-operators.com/
Henningsen, A. (2020): Introduction to Econometric Production Analysis with R -> https://leanpub.com/ProdEconR/
Hoffmann, J.P. (2022): Linear Regression Models - Applications in R -> https://doi.org/10.1201/9781003162230
Hothorn, T. y Everitt, B.S. (2014): A Handbook of Statistical Analysis using R -> https://rdrr.io/cran/HSAUR3/
Horton, N.J. y Kleinman K. (2015): Using R and RStudio for Data Management, Statistical Analysis, and Graphics -> https://nhorton.people.amherst.edu/r2/
Huntington-Klein, N. (2022): The Effect - An Introduction to Research Design and Causality -> https://theeffectbook.net/
Hyndman, R.J. y Athanasopoulos, G. (20251): Forecasting: Principles and Practice -> https://otexts.com/fpp3/ [ESP: https://otexts.com/fppsp/]
Huffaker, R.R. et al. (2017): Nonlinear Time Series Analysis with R -> https://global.oup.com/academic/product/nonlinear-time-series-analysis-with-r-9780198782933
Inchausti, P. (2023): Statistical Modeling with R -> https://sites.google.com/view/statistical-modeling-with-r/home
Irizarry, R.A. (2023): Introducción a la Ciencia de Datos -> https://rafalab.dfci.harvard.edu/dslibro/
Irizarry, R.A. (2025): Introduction to Data Science - Part 1 -> https://rafalab.dfci.harvard.edu/dsbook-part-1/
Irizarry, R.A. (2025): Introduction to Data Science - Part 2 -> https://rafalab.dfci.harvard.edu/dsbook-part-2/
Ismay, C. et al. (2025): Statistical Inference via Data Science -> https://moderndive.com/v2/
Jones, E. et al. (2023): The R Book -> https://bcs.wiley.com/he-bcs/Books?action=index&bcsId=12402&itemId=1119634326
Kabacoff, R. (2024): R in Action -> https://github.com/Rkabacoff/RiA3
Kabacoff, R. (2024): Modern Data Visualization with R -> https://rkabacoff.github.io/datavis/
Kelejian, H. y Piras, G. (2017): Spatial Econometrics -> https://www.sciencedirect.com/book/9780128133873/spatial-econometrics
Kuhn, M. y Johnson, K. (2019): Feature Engineering and Selection - A Practical Approach for Predictive Models -> http://www.feat.engineering/
Kuhn, M. y Silge, J. (2023): Tidy Modeling with R -> https://www.tmwr.org/
Kitagawa, G. (2021): Introduction to Time Series Modeling with Applications in R -> https://cran.r-project.org/web/packages/TSSS/
Kleiber, C. y Zeileis, A. (2008): Applied Econometrics with R -> https://www.zeileis.org/teaching/AER/
Lawson, A.B (2021): Using R for Bayesian Spatial and Spatio-Temporal Health Data -> https://github.com/Andrew9Lawson??
Lin, H. y Lin, M. (2023): Practitioner's Guide to Data Science -> https://scientistcafe.com/ids/
Lovelace, R. et al. (2024): Geocomputation with R -> https://r.geocompx.org/
Lyubchich V. y Gel, Y. R. (2023): Time Series Analysis -> https://vlyubchich.github.io/tsar/
Marin, J.M. y Robert, C. (2014): Bayesian Essentials with R -> https://www.ceremade.dauphine.fr/~xian/BCS/
Matloff, L. (2017): Statistical Regression and Classification -> http://heather.cs.ucdavis.edu/regclass.html
Matloff, L. (2020): Probability and Statistics for Data Science - Math + R + Data -> http://heather.cs.ucdavis.edu/probstatbook
Maronna, R.A. et al. (2019): Robust Statistics - Theory and Methods with R -> https://www.wiley.com/en-us/Robust+Statistics%3A+Theory+and+Methods+%28with+R%29%2C+2nd+Edition-p-9781119214663
McElreath, R. (2020): Statistical Rethinking - A Bayesian Course with Examples in R and STAN -> https://xcelab.net/rm/statistical-rethinking/
Moore, D.F. (2016): Applied Survival Analysis Using R -> https://www.springer.com/us/book/9783319312439
Moraga, P. (2019): Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny -> https://www.paulamoraga.com/book-geospatial/
Moraga, P. (2024): Spatial Statistics for Data Science - Theory and Practice with R -> https://www.paulamoraga.com/book-spatial/
Navarro, D. (2021): Learning Statistics with R -> https://learningstatisticswithr.com/
Neth, H. (2024): Data Science for Psychologists -> https://bookdown.org/hneth/ds4psy/
Oswald, F. et al. (2020): Introduction to Econometrics with R -> https://scpoecon.github.io/ScPoEconometrics/
Oyana, T.J. (2021): Spatial Analysis with R -> https://www.routledge.com/Spatial-Analysis-with-R-Statistics-Visualization-and-Computational-Methods/Oyana/p/book/9780367860851
Paez, A. (2024): An Introduction to Spatial Data Analysis and Statistics: A Course in R -> https://paezha.github.io/spatial-analysis-r/
Pathak, M.A. (2014): Beginning Data Science with R -> https://www.springer.com/gp/book/9783319120652
Pfaff, B. (2016): Financial Risk Modelling and Portfolio Optimization with R -> https://www.pfaffikus.de/books/jwex2/
Pebesma, E. y Bivand, R. (2023): Spatial Data Science - With Applications in R -> https://r-spatial.org/book/
Perpiñán, O. (2018): Displaying Time Series, Spatial, and Space-Time Data with R -> https://github.com/oscarperpinan/bookvis
Racine, R.S. (2019): Reproducible Econometrics Using R -> https://global.oup.com/us/companion.websites/9780190900663/
Rasch, D. et al. (2020): Applied Statistics -> https://onlinelibrary.wiley.com/doi/book/10.1002/9781119551584
Regenstein, J.K. Jr. (2019): Reproducible Finance with R -> http://www.reproduciblefinance.com/
Riazoshams, H. et al. (2019): Robust Nonlinear Regression with Applications using R -> https://www.wiley.com/en-us/Robust+Nonlinear+Regression%3A+with+Applications+using+R-p-9781118738061
Roback, P. y Legler, J. (2021): Beyond Multiple Linear Regression -> https://bookdown.org/roback/bookdown-BeyondMLR/
Ruppert, D. y Matteson, D.S. (2015): Statistics and Data Analysis for Financial Engineering -> https://people.orie.cornell.edu/davidr/SDAFE2/
Scheuch, S. et al. (2023): Tidy Finance with R -> https://www.tidy-finance.org/
Sievert, C. (2019): Interactive web- based data visualization with R, plotly, and shiny -> https://plotly-r.com/
Singh, A. (2022): R for Data Analytics -> https://rforanalytics.com/
Shipunov, A. (2020): Visual Statistics -> http://ashipunov.me/shipunov/software/r/r-en.htm
Shumway, R.H. y Stoffer, D.S. (2017): Time Series Analysis and Its Applications -> https://www.stat.pitt.edu/stoffer/tsa4/ ; https://github.com/nickpoison/tsa4
Thulin, M. (2025): Modern Statistics with R -> https://www.modernstatisticswithr.com/
Timbers, T. at al. (2024): Data Science: A First Introduction (with R) -> https://datasciencebook.ca/
Torgo, L. (2017): Data Mining with R - Learning with Case Studies -> http://ltorgo.github.io/DMwR2/
Tsay, R.S. (2013): An Introduction to Analysis of Financial Data with R -> https://faculty.chicagobooth.edu/ruey-s-tsay/research/an-introduction-to-analysis-of-financial-data-with-r
Tsay, R.S. (2014): Multivariate Time Series Analysis With R and Financial Applications -> https://faculty.chicagobooth.edu/ruey-s-tsay/research/multivariate-time-series-analysis-with-r-and-financial-applications
Verzani, J. (2014): Using R for Introductory Statistics -> https://www.routledge.com/Using-R-for-Introductory-Statistics/Verzani/p/book/9781466590731
van Buuren, S. (2018): Flexible Imputation of Missing Data -> https://stefvanbuuren.name/publication/vanbuuren-2018/
Wang, X. et al. (2018): Bayesian Regression Modeling with INLA -> https://github.com/julianfaraway/brinla
Weisberg, S. (2014): Applied Linear Regression -> http://users.stat.umn.edu/~sandy/alr4ed/
- Fox, J. y Weisberg, S. (2019): An R Companion to Applied Regression -> https://socialsciences.mcmaster.ca/jfox/Books/Companion/
Wickham, H. (2023): Advanced R -> https://adv-r.hadley.nz/
Wikle, C.K. et al. (2019): Spatio-Temporal Statistics with R -> https://spacetimewithr.org/
Wimberly, M.C. (2023): Geographic Data Science with R -> https://bookdown.org/mcwimberly/gdswr-book/
Westfall, P.H. y Arias, A.L. (2020): Understanding Regression Analysis - A Conditional Distribution Approach -> https://github.com/andrea2719
Wood, S.N. (2017): Generalized Linear Models -> https://www.maths.ed.ac.uk/~swood34/igam/index.html
Woodward, W.A. et al. (2017): Applied Time Series Analysis with R -> https://www.texasoft.com/ATSA/index.html
Wu, C.O. y Tian, X. (2018): Nonparametric Models for Longitudinal Data -> https://github.com/npmldabook/rcodes
Yamagata, Y. y Seya, H. (2020): Spatial Analysis using Big Data -> https://www.sciencedirect.com/book/9780128131275/spatial-analysis-using-big-data
Zeileis, A. y Kleiber, C. (2023): Applied Microeconometrics with R -> https://discdown.org/microeconometrics/
Zumel, N. y Mount, J. (2020): Practical Data Science with R -> https://github.com/WinVector/PDSwR2

PYTHON

Adhikari, A. et al. (2022): Computational and Inferential Thinking - The Foundations of Data Science -> https://inferentialthinking.com/
Aldridge, I. y Avellaneda, M. (2021): Big Data Science in Finance -> https://www.wiley.com/en-mv/Big+Data+Science+in+Finance-p-9781119602972
Al-Taie, M.Z. y Kadry, S. (2017): Python for Graph and Network Analysis -> https://www.springer.com/gp/book/9783319530031
Brownlee, J. (2020): Introduction to Time Series Forecasting with Python -> https://machinelearningmastery.com/introduction-to-time-series-forecasting-with-python/
Cady, F. (2017): The Data Science Handbook -> https://github.com/field-cady/the_data_science_handbook
Denis, D.J. (2021): Applied Univariate, Bivariate, and Multivariate Statistics Using Python -> http://www.datapsyc.com/
Donoghue, T. et al. (2022): Data Science in Practice -> https://datascienceinpractice.github.io/docs/
Dorman, M. et al. (2024): Geocomputation with Python -> https://py.geocompx.org/
Downey, A.B. (2014): Think Stats -> https://greenteapress.com/thinkstats2/html/
Downey, A.B. (2021): Think Bayes - Bayesian Statistics in Python -> http://allendowney.github.io/ThinkBayes2/
Downey, A.B. (2023): Elements of Data Science -> https://allendowney.github.io/ElementsOfDataScience/
Faraway, J. (2021): Linear Models with Python -> https://julianfaraway.github.io/LMP/
Fenner, M. (2020): Machine Learning with Python for Everyone -> https://github.com/mfenner1/mlwpy_code
Gagolewski, M. (2024): Minimalist Data Wrangling with Python -> https://datawranglingpy.gagolewski.com/
Géron, A. (2019): Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow -> https://github.com/ageron/handson-ml2
Grus, J. (2019): Data Science from Scratch - First Principles in Python -> https://github.com/joelgrus/data-science-from-scratch
Haslwanter, T. (2016): An Introduction to Statistics with Python -> https://github.com/thomas-haslwanter/statsintro_python
Hilpisch, Y. (2019): Python for Finance -> https://github.com/yhilpisch/py4fi2nd/
Hyndman, R.J. et al. (2025): Forecasting: Principles and Practice, the Pythonic Way -> https://otexts.com/fpppy/
Igual, L. y Seguí, S. (2017): Introduction to Data Science -> https://github.com/DataScienceUB/introduction-datascience-python-book
Karsdorp, F. et al. (2021): Humanities Data Analysis - Case Studies with Python -> https://zenodo.org/record/3563075
Kenett, R.S. et al. (2022): Modern Statistics - A Computer-Based Approach with Python -> https://gedeck.github.io/mistat-code-solutions/ModernStatistics/
Lau S. et al. (2023): Learning Data Science -> https://learningds.org/
Massaron, L. y Boschetti, A. (2016): Regression Analysis with Python -> https://www.oreilly.com/library/view/regression-analysis-with/9781785286315/
Müller, A.C. y Guido, S. (2017): Introduction to Machine Learning with Python -> https://github.com/amueller/introduction_to_ml_with_python
Navlani, A. et al. (2021): Python Data Analysis -> https://github.com/PacktPublishing/Python-Data-Analysis-Third-Edition
Py4DS Community (2022): Python for Data Science -> https://aeturrell.github.io/python4DS/
Raters, F. y Manssen, E. (2022): Python for Econometrics in Economics -> https://pyecon.org/
Rey, S.J. et al. (2023): Geographic Data Science with Python - > https://geographicdata.science/book/
Rogel-Salazar, J. (2017): Data Science and Analytics with Python -> https://jrogel.com/data-science-and-analytics-with-python/
Rogel-Salazar, J. (2020): Advanced Data Science and Analytics with Python -> https://jrogel.com/data-science-and-analytics-with-python/
Sargent, T.J. y Stachurski, J. (2021): Quantitative Economics with Python -> https://quantecon.org/python-lectures/
Schwarz, J.S., Chapman, C. y Feit E.M. (2020): Python for Marketing Research and Analytics -> https://python-marketing-research.github.io/
Shea, J.M. (2024): Foundation of Data Science with Python -> https://www.fdsp.net/intro.html
Sheppard, K. (2021): Financial Econometrics -> https://www.kevinsheppard.com/teaching/mfe/
Tenkanen, H. (2024): Introduction to Python for Geographic Data Analysis -> https://pythongis.org/
Timbers, T. at al. (2025): Data Science: A First Introduction with Python-> https://datasciencebook.ca/
Tipoe, E. y Becker, R. (2023): Doing Economics -> https://www.core-econ.org/doing-economics/
Turrell, A. (2022): Python for Data Science -> https://aeturrell.github.io/python4DS/
Turrell, A. (2022): Coding for Economists -> https://aeturrell.github.io/coding-for-economists/
Unpingco, J. (2019): Python for Probability, Statistics and Machine Learning -> https://github.com/unpingco/Python-for-Probability-Statistics-and-Machine-Learning-2E
VanderPlas, J. (2018): Python Data Science Handbook -> https://github.com/jakevdp/PythonDataScienceHandbook

R y PYTHON

Agresti, A. y Kateri, M. (2022): Foundations of Statistics for Data Scientists - With R and Python -> http://stat4ds.rwth-aachen.de/
Alexander, R. (2023): Telling Stories with Data -> https://tellingstorieswithdata.com/
Arel-Bundock, V. et al. (2025): Model to Meaning -> https://marginaleffects.com/
Biecek, P. y Burzykowski, T. (2020): Explanatory Model Analysis -> https://ema.drwhy.ai/
Brown, T.R. (2023): An Introduction to R and Python for Data Analysis: A Side By Side Approach -> https://randpythonbook.netlify.app/
Bruce, P. et al. (2020): Practical Statistics for Data Science -> https://github.com/gedeck/practical-statistics-for-data-scientists
Buisson, F. (2021): Behavioral Data Analysis with R and Python -> https://github.com/FlorentBuissonOReilly/BehavioralDataAnalysis
Buteikis, A. (2024): Practical Econometrics and Data Science -> https://web.vu.lt/mif/a.buteikis/category/pe-book/
Chan, S. (2021): Introduction to Probability for Data Science -> https://probability4datascience.com/
Chernozhukov, V. et al. (2024): Applied Causal Inference Powered by ML and AI -> https://causalml-book.org/
Clark, M. y Berry, S. (2025): Models Demystified -> https://m-clark.github.io/book-of-models/
Cremonini, M. (2024): Data Science Fundamentals with R, Python, and Open Data -> https://bcs.wiley.com/he-bcs/Books?action=index&bcsId=12777&itemId=1394213247
GeocompX (2024): Geocomputation (with R and Python) -> https://geocompx.org/
Gelman, A. et al. (2014): Bayesian Data Analysis -> http://www.stat.columbia.edu/~gelman/book/
Huang, S. y Deng, H. (2021): Data Analytics: A Small Data Approach -> https://dataanalyticsbook.info/
Huntington-Klein, N. (2023): The Effect: An Introduction to Research Design and Causality -> https://theeffectbook.net/
Lin, H. y Li, M. (2023): Practitioner's Guide to Data Science -> https://scientistcafe.com/ids/
McNulty, K. (2021): Handbook of Regression Modeling in People Analytics -> https://peopleanalytics-regression-book.org/
McNulty, K. (2022): Handbook of Graphs and Networks in People Analytics -> https://ona-book.org/
Mount, G. (2021): Advancing into Analytics - From Excel to Python and R -> http://stringfestanalytics.com/book/
Nielsen, A. (2020): Practical Time Series Analysis -> https://github.com/PracticalTimeSeriesAnalysis/BookRepo
Pebesma, E. y Bivand, R. (2023): Spatial Data Science with Applications in R y Python -> https://r-spatial.org/python/
Scavetta, R.J. y Angelow, B, (2021): Python and R for the Modern Data Scientist - The Best of Both Worlds -> https://github.com/moderndatadesign/PyR4MDS
Scheuch, C. et al. (2023): Tidy Finance -> https://www.tidy-finance.org/
Shah, C. (2020): A Hands-On Introduction to Data Science -> https://www.cambridge.org/highereducation/books/handson-introduction-to-data-science/9D55C29C653872F13289EA7909953842
Zeileis, A. et al., (2023): Data Analytics -> https://discdown.org/dataanalytics/
Zhang, A. et al. (2021): Dive into Deep Learning -> https://d2l.ai/index.html
Zhang, N. (2021): A Tour of Data Science - Learn R and Python in Parallel -> https://www.routledge.com/A-Tour-of-Data-Science-Learn-R-and-Python-in-Parallel/Zhang/p/book/9780367895860

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