book.bib

@Book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.name/knitr/},
}

@book{matloff_r_book,
author = {Matloff, Norman},
title = {The Art of R Programming: A Tour of Statistical Software Design},
year = {2011},
isbn = {1593273843},
publisher = {No Starch Press},
address = {USA},
edition = {1st},
abstract = {From the Author: Why Use R for Your Statistical Work? As the Cantonese say, yauh peng, yauh leng, which means both inexpensive and beautiful. Why use anything else? R has a number of virtues: It is a public-domain implementation of the widely regarded S statistical language, and the R/S platform is a de facto standard among professional statisticians. It is comparable, and often superior, in power to commercial products in most of the significant senses -- variety of operations available, programmability, graphics, and so on. It is available for the Windows, Mac, and Linux operating systems. In addition to providing statistical operations, R is a general-purpose programming language, so you can use it to automate analyses and create new functions that extend the existing language features. R includes a library of several thousand user-contributed packages. It incorporates features found in object-oriented and functional programming languages. R is capable of producing beautiful graphics for your presentations, reports or articles. }
}


@book{intro_to_r,
author = {Venables, William N. and Smith, David M.},
title = {An Introduction to R},
year = {2009},
isbn = {0954612086},
publisher = {Network Theory Ltd.},
edition = {2nd},
abstract = {This tutorial manual provides a comprehensive introduction to R, a software package for statistical computing and graphics. R supports a wide range of statistical techniques, and is easily extensible via user-defined functions written in its own language or using C, C++ or Fortran. One of R's strengths is the ease with which well-designed publication-quality plots can be produced. This is a printed copy of the tutorial manual from the R distribution, with additional examples, notes and corrections. It is based on R version 2.9.0, released April 2009. R is free software, distributed under the terms of the GNU General Public License (GPL). It can be used with GNU/Linux, Unix and Microsoft Windows. All the money raised from the sale of this book supports the development of free software and documentation.}
}

  
@Article{         harris2020array,
 title         = {Array programming with {NumPy}},
 author        = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
                 van der Walt and Ralf Gommers and Pauli Virtanen and David
                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
                 Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
                 R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
                 G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
                 Travis E. Oliphant},
 year          = {2020},
 month         = sep,
 journal       = {Nature},
 volume        = {585},
 number        = {7825},
 pages         = {357--362},
 doi           = {10.1038/s41586-020-2649-2},
 publisher     = {Springer Science and Business Media {LLC}},
 url           = {https://doi.org/10.1038/s41586-020-2649-2}
}

  
@book{py_ds_handbook,
author = {VanderPlas, Jake},
title = {Python Data Science Handbook: Essential Tools for Working with Data},
year = {2016},
isbn = {1491912057},
publisher = {O'Reilly Media, Inc.},
edition = {1st},
abstract = {For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use:IPython and Jupyter: provide computational environments for data scientists using PythonNumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in PythonPandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in PythonMatplotlib: includes capabilities for a flexible range of data visualizations in PythonScikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms}
}

@book{wickham2014advanced,
  title={Advanced R},
  author={Wickham, H.},
  isbn={9781466586963},
  lccn={2012278240},
  series={Chapman \& Hall/CRC The R Series},
  url={https://books.google.com/books?id=PFHFNAEACAAJ},
  year={2014},
  publisher={Taylor \& Francis}
}

@book{struc_and_interp, author = {Abelson, Harold and Sussman, Gerald J.}, title = {Structure and Interpretation of Computer Programs}, year = {1996}, isbn = {0262011530}, publisher = {MIT Press}, address = {Cambridge, MA, USA}, edition = {2nd}, abstract = {From the Publisher:With an analytical and rigorous approach to problem solving and programming techniques,this book is oriented toward engineering. Structure and Interpretation of Computer Programs emphasizes the central role played by different approaches to dealing with time in computational models. Its unique approach makes it appropriate for an introduction to computer science courses,as well as programming languages and program design.} }

@book{monte-carlo-stat-methods, author = {Robert, Christian P. and Casella, George}, title = {Monte Carlo Statistical Methods (Springer Texts in Statistics)}, year = {2005}, isbn = {0387212396}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg} }


@book{ggplot2,
author = {Hadley Wickham},
title = {ggplot2: Elegant Graphics for Data Analysis},
publisher = {Springer-Verlag New York},
year = {2016},
isbn = {978-3-319-24277-4},
url = {https://ggplot2.tidyverse.org},
}

@Article{Hunter:2007,
  Author    = {Hunter, J. D.},
  Title     = {Matplotlib: A 2D graphics environment},
  Journal   = {Computing in Science \& Engineering},
  Volume    = {9},
  Number    = {3},
  Pages     = {90--95},
  abstract  = {Matplotlib is a 2D graphics package used for Python for
  application development, interactive scripting, and publication-quality
  image generation across user interfaces and operating systems.},
  publisher = {IEEE COMPUTER SOC},
  doi       = {10.1109/MCSE.2007.55},
  year      = 2007
}


@book{gog,
author = {Wilkinson, Leland},
title = {The Grammar of Graphics (Statistics and Computing)},
year = {2005},
isbn = {0387245448},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg}
}


@Manual{cpm,
    title = {cPseudoMaRg: Constructs a Correlated Pseudo-Marginal Sampler},
    author = {Taylor Brown},
    year = {2021},
    note = {R package version 1.0.0},
    url = {https://CRAN.R-project.org/package=cPseudoMaRg}
  }
  
  @book{hunt2000pragmatic,
  added-at = {2013-11-29T20:55:50.000+0100},
  address = {Boston [etc.]},
  author = {Hunt, Andrew and Thomas, David},
  biburl = {https://www.bibsonomy.org/bibtex/2f089d7f6f61cad0258c08477d2f920a7/admogar},
  description = {The Pragmatic Programmer: From Journeyman to Master: Andrew Hunt, David Thomas: 9780201616224: Amazon.com: Books},
  interhash = {e614934d54a2ffbddf23a42d0dc729ff},
  intrahash = {f089d7f6f61cad0258c08477d2f920a7},
  isbn = {020161622X 9780201616224},
  keywords = {programming software},
  publisher = {Addison-Wesley},
  refid = {806497391},
  timestamp = {2013-12-02T09:48:03.000+0100},
  title = {The Pragmatic programmer : from journeyman to master},
  url = {http://www.amazon.com/The-Pragmatic-Programmer-Journeyman-Master/dp/020161622X},
  year = 2000
}

@Manual{gradeR,
    title = {gradeR: Helps Grade Assignment Submissions that are R Scripts},
    author = {Taylor Brown},
    year = {2020},
    note = {R package version 1.0.9},
    url = {https://CRAN.R-project.org/package=gradeR},
  }

@misc{uci_data,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences" } 

@misc{otter,
title = "Otter-Grader: A Python and R autograding solution",
author = "Christopher Pyles",
organization = "UC Berkeley Data Science Education Program",
year = 2019,
url = "https://github.com/ucbds-infra/otter-grader"
}



@article{wine_data,
  added-at = {2020-02-20T00:00:00.000+0100},
  author = {Cortez, Paulo and Cerdeira, António and Almeida, Fernando and Matos, Telmo and Reis, José},
  biburl = {https://www.bibsonomy.org/bibtex/2816300e541b2992accb28304b8a112b7/dblp},
  ee = {https://doi.org/10.1016/j.dss.2009.05.016},
  interhash = {6c135058ea6a80e756e5c3479a8e7cac},
  intrahash = {816300e541b2992accb28304b8a112b7},
  journal = {Decis. Support Syst.},
  keywords = {dblp},
  number = 4,
  pages = {547-553},
  timestamp = {2020-02-21T11:40:45.000+0100},
  title = {Modeling wine preferences by data mining from physicochemical properties.},
  url = {http://dblp.uni-trier.de/db/journals/dss/dss47.html#CortezCAMR09},
  volume = 47,
  year = 2009
}


@InProceedings{horseshoe,
  title = 	 {Handling Sparsity via the Horseshoe},
  author = 	 {Carvalho, Carlos M. and Polson, Nicholas G. and Scott, James G.},
  booktitle = 	 {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics},
  pages = 	 {73--80},
  year = 	 {2009},
  editor = 	 {van Dyk, David and Welling, Max},
  volume = 	 {5},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA},
  month = 	 {16--18 Apr},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v5/carvalho09a/carvalho09a.pdf},
  url = 	 {https://proceedings.mlr.press/v5/carvalho09a.html},
  abstract = 	 {This paper presents a general, fully Bayesian framework for sparse supervised-learning problems based on the horseshoe prior. The horseshoe prior is a member of the family of multivariate scale mixtures of normals, and is therefore closely related to widely used approaches for sparse Bayesian learning, including, among others, Laplacian priors (e.g. the LASSO) and Student-t priors (e.g. the relevance vector machine). The advantages of the horseshoe are its robustness at handling unknown sparsity and large outlying signals. These properties are justifed theoretically via a representation theorem and accompanied by comprehensive empirical experiments that compare its performance to benchmark alternatives.}
}

@misc{misc_heart_disease_45,
  author       = {Janosi, Andras and Steinbrunn, William and Pfisterer, Matthias and Detrano, Robert},
  title        = {{Heart Disease}},
  year         = {1988},
  howpublished = {UCI Machine Learning Repository}
}klkj


@misc{misc_iris_53,
  author       = {Fisher, R.A. \& Creator, Test},
  title        = {{Iris}},
  year         = {1988},
  howpublished = {UCI Machine Learning Repository}
}

@misc{misc_adult_2,
  title        = {{Adult}},
  year         = {1996},
  howpublished = {UCI Machine Learning Repository}
}

@misc{misc_car_evaluation_19,
  title        = {{Car Evaluation}},
  year         = {1997},
  howpublished = {UCI Machine Learning Repository}
}

@misc{misc_chess,
  title        = {{Chess (King-Rook vs. King-Pawn)}},
  year         = {1989},
  howpublished = {UCI Machine Learning Repository}
}

@article{woodbury,
author = {Louis Guttman},
title = {{Enlargement Methods for Computing the Inverse Matrix}},
volume = {17},
journal = {The Annals of Mathematical Statistics},
number = {3},
publisher = {Institute of Mathematical Statistics},
pages = {336 -- 343},
year = {1946},
doi = {10.1214/aoms/1177730946},
URL = {https://doi.org/10.1214/aoms/1177730946}
}

@ARTICLE{Jones72astatistical,
    author = {Karen Spärck Jones},
    title = {A statistical interpretation of term specificity and its application in retrieval},
    journal = {Journal of Documentation},
    year = {1972},
    volume = {28},
    pages = {11--21}
}

@incollection{SocherEtAl2013:RNTN,
title = {{Parsing With Compositional Vector Grammars}},
author = {Richard Socher and Alex Perelygin and Jean Wu and Jason Chuang and Christopher Manning and Andrew Ng and Christopher Potts},
booktitle = {{EMNLP}},
year = {2013}
}

@misc{gspc_data,
  title = {GSPC DATA},
  howpublished = {\url{https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC}},
  note = {Accessed: 2021-10-03},
  year = 2021
}

  
@misc{TFDS,
  title = { {TensorFlow Datasets}, A collection of ready-to-use datasets},
  howpublished = {\url{https://www.tensorflow.org/datasets} },
  year = 2021
}

@book{GelmanHill:2007,
  author = {Gelman, Andrew and Hill, Jennifer},
  title = {Data Analysis Using Regression and Multilevel/Hierarchical Models},
  publisher = {Cambridge University Press},
  series = {Analytical methods for social research},
  year = 2007
}

@Manual{hmisc,
  title = {Hmisc: Harrell Miscellaneous},
  author = {Frank E {Harrell Jr} and with contributions from Charles Dupont and many others.},
  year = {2021},
  note = {R package version 4.5-0},
  url = {https://CRAN.R-project.org/package=Hmisc},
}

@article{bootstrap,
author = {B. Efron},
title = {{Bootstrap Methods: Another Look at the Jackknife}},
volume = {7},
journal = {The Annals of Statistics},
number = {1},
publisher = {Institute of Mathematical Statistics},
pages = {1 -- 26},
keywords = {bootstrap, discriminant analysis, error rate estimation, jackknife, Nonlinear regression, nonparametric variance estimation, Resampling, subsample values},
year = {1979},
doi = {10.1214/aos/1176344552},
URL = {https://doi.org/10.1214/aos/1176344552}
}

@article{Nadaraya,
author = {Nadaraya, E. A.},
title = {On Estimating Regression},
journal = {Theory of Probability \& Its Applications},
volume = {9},
number = {1},
pages = {141-142},
year = {1964},
doi = {10.1137/1109020},

URL = {
        https://doi.org/10.1137/1109020

},
eprint = {
        https://doi.org/10.1137/1109020
}
}



@article{Watson,
 ISSN = {0581572X},
 URL = {http://www.jstor.org/stable/25049340},
 abstract = {Few would deny that the most powerful statistical tool is graph paper. When however there are many observations (and/or many variables) graphical procedures become tedious. It seems to the author that the most characteristic problem for statisticians at the moment is the development of methods for analyzing the data poured out by electronic observing systems. The present paper gives a simple computer method for obtaining a "graph" from a large number of observations.},
 author = {Geoffrey S. Watson},
 journal = {Sankhyā: The Indian Journal of Statistics, Series A (1961-2002)},
 number = {4},
 pages = {359--372},
 publisher = {Springer},
 title = {Smooth Regression Analysis},
 volume = {26},
 year = {1964}
}

@ARTICLE{metropolis,
       author = {{Metropolis}, Nicholas and {Rosenbluth}, Arianna W. and {Rosenbluth}, Marshall N. and {Teller}, Augusta H. and {Teller}, Edward},
        title = "{Equation of State Calculations by Fast Computing Machines}",
      journal = {\jcp},
         year = 1953,
        month = jun,
       volume = {21},
       number = {6},
        pages = {1087-1092},
          doi = {10.1063/1.1699114},
       adsurl = {https://ui.adsabs.harvard.edu/abs/1953JChPh..21.1087M},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@book{rfords,
author = {Wickham, Hadley and Grolemund, Garrett},
title = {R for Data Science: Import, Tidy, Transform, Visualize, and Model Data},
year = {2017},
isbn = {1491910399},
publisher = {O'Reilly Media, Inc.},
edition = {1st},
abstract = {Learn how to use R to turn raw data into insight, knowledge, and understanding. This
book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed
to work together to make data science fast, fluent, and fun. Suitable for readers
with no previous programming experience, R for Data Science is designed to get you
doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund
guide you through the steps of importing, wrangling, exploring, and modeling your
data and communicating the results. Youll get a complete, big-picture understanding
of the data science cycle, along with basic tools you need to manage the details.
Each section of the book is paired with exercises to help you practice what youve
learned along the way. Youll learn how to: Wrangletransform your datasets into a form
convenient for analysisProgramlearn powerful R tools for solving data problems with
greater clarity and easeExploreexamine your data, generate hypotheses, and quickly
test themModelprovide a low-dimensional summary that captures true "signals" in your
datasetCommunicatelearn R Markdown for integrating prose, code, and results}
}

  
@inproceedings{West1989BayesianFA,
  title={Bayesian forecasting and dynamic models},
  author={Michael A. West and Jeff Harrison},
  year={1989}
}


@article{kalman_filt,
    author = {Kalman, R. E.},
    title = "{A New Approach to Linear Filtering and Prediction Problems}",
    journal = {Journal of Basic Engineering},
    volume = {82},
    number = {1},
    pages = {35-45},
    year = {1960},
    month = {03},
    abstract = "{The classical filtering and prediction problem is re-examined using the Bode-Shannon representation of random processes and the "state-transition" method of analysis of dynamic systems. New results are: (1) The formulation and methods of solution of the problem apply without modification to stationary and nonstationary statistics and to growing-memory and infinite-memory filters. (2) A nonlinear difference (or differential) equation is derived for the covariance matrix of the optimal estimation error. From the solution of this equation the co-efficients of the difference (or differential) equation of the optimal linear filter are obtained without further calculations. (3) The filtering problem is shown to be the dual of the noise-free regulator problem. The new method developed here is applied to two well-known problems, confirming and extending earlier results. The discussion is largely self-contained and proceeds from first principles; basic concepts of the theory of random processes are reviewed in the Appendix.}",
    issn = {0021-9223},
    doi = {10.1115/1.3662552},
    url = {https://doi.org/10.1115/1.3662552},
    eprint = {https://asmedigitalcollection.asme.org/fluidsengineering/article-pdf/82/1/35/5518977/35\_1.pdf},
}


@book{Lutz13,
  abstract = {Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz's popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. This easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3--- the latest releases in the 3.X and 2.X lines---plus all other releases in common use today. You'll also learn some advanced language features that recently have become more common in Python code.},
  added-at = {2017-04-30T14:21:09.000+0200},
  address = {Beijing},
  author = {Lutz, Mark},
  biburl = {https://www.bibsonomy.org/bibtex/2a4baf4cad6c7f9af2f1048f9af7c4bf7/flint63},
  edition = 5,
  file = {O'Reilly eBook:2013/Lutz13.pdf:PDF;O'Reilly Product page:http\://shop.oreilly.com/product/0636920028154.do:URL},
  groups = {public},
  interhash = {ba74a73bacdda726264b3fb60cc2449a},
  intrahash = {a4baf4cad6c7f9af2f1048f9af7c4bf7},
  isbn = {978-1-4493-5573-9},
  keywords = {01841 103 book safari software development python intro},
  publisher = {O'Reilly},
  timestamp = {2018-04-16T12:20:34.000+0200},
  title = {Learning Python},
  url = {https://www.safaribooksonline.com/library/view/learning-python-5th/9781449355722/},
  username = {flint63},
  year = 2013
}

@Book{pythonregexprs,
 author = {López, Félix},
 title = {Mastering Python regular expressions : leverage regular expressions in Python even for the most complex features},
 publisher = {Packt Pub},
 year = {2014},
 address = {Birmingham, UK},
 isbn = {9781783283163}
 }


@article{mid5,
author = {Glenn Palmer and Roseanne W McManus and Vito D’Orazio and Michael R Kenwick and Mikaela Karstens and Chase Bloch and Nick Dietrich and Kayla Kahn and Kellan Ritter and Michael J Soules},
title ={The MID5 Dataset, 2011–2014: Procedures, coding rules, and description},
journal = {Conflict Management and Peace Science},
volume = {0},
number = {0},
pages = {0738894221995743},
year = {0},
doi = {10.1177/0738894221995743},

URL = { 
        https://doi.org/10.1177/0738894221995743
    
},
eprint = { 
        https://doi.org/10.1177/0738894221995743
    
}
,
    abstract = { This article introduces the latest iteration of the most widely used dataset on interstate conflicts, the Militarized Interstate Dispute (MID) 5 dataset. We begin by outlining the data collection process used in the MID5 project. Next, we discuss some of the most challenging cases that we coded and some updates to the coding manual that resulted. Finally, we provide descriptive statistics for the new years of the MID data. }
}


@article{student1908probable,
  title={The probable error of a mean},
  author={Student},
  journal={Biometrika},
  pages={1--25},
  year={1908},
  publisher={JSTOR}
} 

@article{impsamping1,
  title={Random sampling (Monte Carlo) techniques in neutron attenuation problems--I.},
  author={H. Kahn},
  journal={Nucleonics},
  year={1950},
  volume={6 5}
}

@article{impsamping2,
title = {Random Sampling (Monte Carlo) Techniques in Neutron Attenuation Problems. II},
author = {Kahn, H},
abstractNote = {},
doi = {},
url = {https://www.osti.gov/biblio/4399718}, 
journal = {Nucleonics (U.S.) Ceased publication},
volume = {Vol: 6, No. 6},
place = {Country unknown/Code not available},
year = {1950},
month = {6}
} 

@misc{albemarle_county_gis_web,
 author = {{Albemarle County Geographic Data Services Office}},
 year = {2021},
 title = {{Albemarle County GIS Web}},
 url = {https://www.albemarle.org/government/community-development/gis-mapping/gis-data},
 note = {Accessed on 12.07.2021}
}

@misc{clay_ford,
  author = {Ford, Clay},
  title = {{ggplot: Files for UVA StatLab workshop, Fall 2016}},
  year = {2016},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/clayford/ggplot2}},
  commit = {ec71264ceb5daac9d770d653841ad7729a7dedbe}
}

@book{trees,
  added-at = {2020-05-07T22:53:11.000+0200},
  address = {Monterey, CA},
  author = {Breiman, L. and Friedman, J. H. and Olshen, R. A. and Stone, C. J.},
  biburl = {https://www.bibsonomy.org/bibtex/27f293aa2bdfd10960ef36928f2795f1d/flashspys},
  interhash = {61f3e6d61ba17bb493014bd1c6dfa670},
  intrahash = {7f293aa2bdfd10960ef36928f2795f1d},
  keywords = {ma treelearning},
  publisher = {Wadsworth and Brooks},
  serial = {bre84a},
  timestamp = {2020-05-07T22:53:11.000+0200},
  title = {Classification and Regression Trees},
  year = 1984
}


@article{knn1,
 ISSN = {03067734, 17515823},
 URL = {http://www.jstor.org/stable/1403797},
 author = {Evelyn Fix and J. L. Hodges},
 journal = {International Statistical Review / Revue Internationale de Statistique},
 number = {3},
 pages = {238--247},
 publisher = {[Wiley, International Statistical Institute (ISI)]},
 title = {Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties},
 volume = {57},
 year = {1989}
}


@ARTICLE{knn2,

  author={Cover, T. and Hart, P.},

  journal={IEEE Transactions on Information Theory}, 

  title={Nearest neighbor pattern classification}, 

  year={1967},

  volume={13},

  number={1},

  pages={21-27},

  doi={10.1109/TIT.1967.1053964}}
  
  
@inproceedings{Anguita2013APD,
 title={A Public Domain Dataset for Human Activity Recognition using Smartphones},
 author={D. Anguita and Alessandro Ghio and L. Oneto and Xavier Parra and Jorge Luis Reyes-Ortiz},
 booktitle={ESANN},
 year={2013}
}

@misc{sas_cars,
  title = {SAS\textsuperscript{\textregistered} Viya\textsuperscript{\textregistered} Example Data Sets},
  howpublished = {\url{https://support.sas.com/documentation/onlinedoc/viya/examples.htm}},
  note = {Accessed: 2021-12-12},
  year = 2021
}


@book{r_in_action,
  added-at = {2019-03-01T00:11:50.000+0100},
  author = {Kabacoff, Robert I.},
  biburl = {https://www.bibsonomy.org/bibtex/2232b9a52cc4b78ae5828dfa8384079d8/gdmcbain},
  citeulike-article-id = {14595538},
  citeulike-attachment-1 = {R_in_Action_Second_Edition.pdf; /pdf/user/gdmcbain/article/14595538/1137658/R_in_Action_Second_Edition.pdf; a75600ac99f432d144055f44b841a7a868044ffc},
  citeulike-linkout-0 = {http://www.worldcat.org/isbn/9781617291388},
  citeulike-linkout-1 = {http://books.google.com/books?vid=ISBN9781617291388},
  citeulike-linkout-2 = {http://www.amazon.com/gp/search?keywords=9781617291388\&index=books\&linkCode=qs},
  citeulike-linkout-3 = {http://www.librarything.com/isbn/9781617291388},
  citeulike-linkout-4 = {http://www.worldcat.org/oclc/915039455},
  comment = {(private-note)command\'{e} de manning.com 2018-05-29, re\c{c}u 06-05},
  edition = {Second},
  file = {R_in_Action_Second_Edition.pdf},
  interhash = {6e499133ad9231d88c37b7bad05290f2},
  intrahash = {232b9a52cc4b78ae5828dfa8384079d8},
  isbn = {9781617291388},
  keywords = {62-04-statistics-explicit-machine-computation-and-programs 62-09-statistics-graphical-methods 65s05-numerical-analysis-graphical-methods r 62-08-computational-methods-for-problems-for-statistics},
  posted-at = {2018-05-29 02:04:55},
  priority = {3},
  publisher = {Manning},
  timestamp = {2020-01-14T22:55:08.000+0100},
  title = {{R in Action}},
  url = {http://www.worldcat.org/isbn/9781617291388},
  year = 2015
}


@book{grolemund2014hands,
  title={Hands-On Programming with R: Write Your Own Functions and Simulations},
  author={Grolemund, G.},
  isbn={9781449359119},
  lccn={2014451870},
  url={https://books.google.com/books?id=S04BBAAAQBAJ},
  year={2014},
  publisher={O'Reilly Media}
}


@book{r_graphics_cookbook,
author = {Chang, Winston},
title = {R Graphics Cookbook},
year = {2013},
isbn = {1449316956},
publisher = {O'Reilly Media, Inc.},
abstract = { Q&A with Winston Chang, author of "R Graphics Cookbook: Practical Recipes for Visualizing Data" Q. Why is your book timely? A. Interest in R for data analysis and visualization has exploded in recent years. In the computer-tech world, computers and networks have made it much easier to gather and organize data, and more and more people have recognized that there's useful information to be found. To illustrate, consider the job "data scientist": this is a job title that didn't even exist five years ago, and now it's one of the hottest tickets on the market. At the same time, there's been a swell of interest in R in its more traditional setting, in science and engineering. I think there are many reasons for this. One, is that there's a growing recognition outside of the computer-programmer world that learning a little programming can save you a lot of time and reduce errors. Another reason is that the last few years have seen an improvement in the user-friendliness of tools for using R. So there's a lot of interest in using R for finding information in data, and visualization an essential tool for doing this. Data visualizations can help you understand your data and find patterns when you're in the exploratory phase of data analysis, and they are essential for communicating your findings to others. Q. What information do you hope that readers of your book will walk away with? A. As my book is a Cookbook, the primary goal is to efficiently present solutions for visualizing data, without demanding a large investment of time from the reader. For many readers, the goal is to just figure out how to make a particular type of graph and be done with it. There are others who will want to gain a deeper understanding of how graphing works in R. For these readers, I've written an appendix on the graphing package ggplot2, which is used extensively in the recipes in the book. This appendix explains some of the concepts in the grammar of graphics, and how they relate to structures common to data visualizations in general. Finally, I hope that readers will find ideas and inspiration for visualizing their data by browsing the pages and looking at the pictures. Q. What's the most exciting/important thing happening in your space? A. I'm excited that R is becoming more and more accessible to users who don't primarily identify as programmers. Many scientists, engineers, and data analysts have outgrown programs that provide canned data analysis routines, and they're turning increasingly to R. The growing popularity of R is part of a virtuous circle: as R gains a larger user base, it encourages people to create better educational materials and programming tools for R, which in turn helps to grow the number of R users. Technology-wise, I'm excited by Shiny, which is a framework for bringing R analyses to the web. (I should mention that this it's part of my job to work on the development of Shiny.) This makes it possible to build interactive applications for data analysis and visualization for users who don't need to know R, or even that the application is backed by R.}
}

@book{r_for_everyone,
author = {Lander, Jared P.},
title = {R for Everyone: Advanced Analytics and Graphics (2nd Edition)},
year = {2017},
isbn = {013454692X},
publisher = {Addison-Wesley Professional},
edition = {2nd},
abstract = {Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality youll need to accomplish 80 percent of modern data tasks. Landers self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. Youll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, youll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this youll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time youre done, you wont just know how to write R programs, youll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using Rs facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available. Normal 0 false false false EN-US X-NONE X-NONE}
}

@book{pandas_guy,
author = {McKinney, Wes},
title = {Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython},
year = {2017},
isbn = {1491957662},
publisher = {O'Reilly Media, Inc.},
edition = {2nd},
abstract = {Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. Youll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. Its ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples}
}

@book{ml_with_python_cookbook,
author = {Albon, Chris},
title = {Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning},
year = {2018},
isbn = {1491989386},
publisher = {O'Reilly Media, Inc.},
edition = {1st},
abstract = {This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. Youll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), nave Bayes, clustering, and neural networks Saving and loading trained models}
}



@Book{python_cookbook,
  author =       "David M. Beazley and Brian K. (Brian Kenneth) Jones",
  title =        "{Python} cookbook: Recipes for mastering {Python 3}",
  publisher =    pub-ORA-MEDIA,
  address =      pub-ORA-MEDIA:adr,
  edition =      "Third",
  pages =        "xvi + 687",
  year =         "2014",
  ISBN =         "1-4493-4037-7 (paperback), 1-4493-5736-9 (e-book)",
  ISBN-13 =      "978-1-4493-4037-7 (paperback), 978-1-4493-5736-8
                 (e-book)",
  LCCN =         "QA76.73.P98 B43 2013eb",
  bibdate =      "Fri Oct 23 15:05:28 MDT 2015",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/python.bib",
  abstract =     "If you need help writing programs in Python 3, or want
                 to update older Python 2 code, this book is just the
                 ticket. Packed with practical recipes written and
                 tested with Python 3.3, this unique cookbook is for
                 experienced Python programmers who want to focus on
                 modern tools and idioms.",
  acknowledgement = ack-nhfb,
  subject =      "Scripting languages (Computer science); Python
                 (Computer program language)",
  tableofcontents = "Data structures and algorithms \\
                 Strings and text \\
                 Numbers, dates, and times \\
                 Iterators and generators \\
                 Files and I/O \\
                 Data encoding and processing \\
                 Functions \\
                 Classes and objects \\
                 Metaprogramming \\
                 Modules and packages \\
                 Network and web programming \\
                 Concurrency \\
                 Utility scripting and system administration \\
                 Testing, debugging, and exceptions \\
                 C extensions",
}