@Manual{RCore,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2017},
url = {https://www.R-project.org/},
}
@misc{pinheiro_nlme:_2012,
title = {nlme: {Linear} and {Nonlinear} {Mixed} {Effects} {Models}},
author = {Pinheiro, Jose and Bates, Douglas and DebRoy, Saikat and Sarkar, Deepayan and Team, R. Core},
year = {2012},
note = {R package version 3.1-104}
}
@misc{free_software_foundation_gnu_2012,
title = {Gnu {Coding} {Standards}},
url = {http://www.gnu.org/prep/standards/},
urldate = {2012-05-31},
author = {Free Software Foundation},
year = {2012}
}
@book{venables_s_2000,
address = {New York},
series = {Statistics and computing},
title = {S {Programming}},
isbn = {0-387-98966-8},
publisher = {Springer},
author = {Venables, W. N},
collaborator = {Ripley, Brian D},
year = {2000},
keywords = {S (Computer program language)}
}
@book{chambers_programming_1998,
address = {New York},
title = {Programming with data: a guide to the {S} language},
isbn = {0-387-98503-4},
shorttitle = {Programming with data},
publisher = {Springer},
author = {Chambers, John M.},
year = {1998},
keywords = {Data processing, S (Computer program language), Statistics}
}
@book{fox_r_2011,
address = {Thousand Oaks CA},
edition = {Second},
title = {An {R} {Companion} to {Applied} {Regression}},
url = {http://socserv.socsci.mcmaster.ca/jfox/Books/Companion},
publisher = {Sage},
author = {Fox, John and Weisberg, Sanford},
year = {2011}
}
@misc{xie_formatr:_2012,
title = {{formatR}: {Format} {R} {Code} {Automatically}},
url = {http://CRAN.R-project.org/package=formatR},
author = {Xie, Yihui},
year = {2012},
note = {R package version 0.4}
}
@misc{chambers_soda:_2012,
title = {{SoDA}: {Functions} and {Exampels} for "{Software} for {Data} {Analysis}"},
url = {http://CRAN.R-project.org/package=SoDA},
author = {Chambers, John M.},
year = {2012},
note = {R package version 1.0-4}
}
@book{chambers_graphical_1983,
address = {Belmont, Calif. : Boston},
series = {The {Wadsworth} statistics/probability series},
title = {Graphical methods for data analysis},
isbn = {0-534-98052-X},
publisher = {Wadsworth International Group ; Duxbury Press},
collaborator = {Chambers, John M.},
year = {1983},
keywords = {Computer graphics, Congresses, Graphic methods Congresses, Statistics}
}
@book{kernighan_c_1988,
edition = {2nd ed.},
title = {C {Programming} {Language}},
isbn = {0-13-110362-8},
publisher = {Prentice Hall},
author = {Kernighan, Brian W. and Ritchie, Dennis M.},
month = apr,
year = {1988}
}
@book{becker_extending_1985,
address = {Monterey, Calif},
series = {Wadsworth statistics/probability series},
title = {Extending the {S} system},
isbn = {0-534-05016-6},
publisher = {Wadsworth Advanced Books and Software},
author = {Becker, Richard A.},
collaborator = {Chambers, John M.},
year = {1985},
keywords = {Data processing, Interactive computer systems, Mathematical statistics, S (Computer system), Statistics}
}
@misc{bates_lme4:_nodate,
title = {lme4: {Linear} mixed-effects models using {Eigen} and {S}4},
url = {http://R-Forge.R-project.org/projects/lme4/},
author = {Bates, Douglas and Maechler, Martin and Bolker, Ben},
note = {R package version 0.999902344-0/r1694}
}
@misc{wickham_style_nodate,
title = {Style {Guide}},
url = {https://github.com/hadley/devtools},
abstract = {Tools to make an R developer's life easier. Contribute to devtools development by creating an account on GitHub.},
urldate = {2012-05-31},
journal = {GitHub},
author = {Wickham, Hadley},
file = {Snapshot:/home/pauljohn/.mozilla/firefox/18uq7hj0.default/zotero/storage/3HWTNSCJ/Style.html:text/html}
}
@book{chambers_software_2008,
address = {New York ; London},
series = {Statistics and computing},
title = {Software for data analysis: programming with {R}},
isbn = {978-0-387-75935-7},
shorttitle = {Software for data analysis},
publisher = {Springer},
author = {Chambers, John M.},
year = {2008},
keywords = {Data processing, Numerical analysis, R (Computer program language)}
}
@book{venables_modern_2002,
address = {New York},
edition = {4th ed},
series = {Statistics and computing},
title = {Modern {Applied} {Statistics} with {S}},
isbn = {0-387-95457-0},
publisher = {Springer},
author = {Venables, W. N and Ripley, Brian D},
year = {2002},
keywords = {Data processing, Mathematical statistics, S (Computer system), Statistics}
}
@book{chambers_computational_1977,
address = {New York},
series = {Wiley series in probability and mathematical statistics},
title = {Computational methods for data analysis},
isbn = {0-471-02772-3},
publisher = {Wiley},
author = {Chambers, John M.},
year = {1977},
keywords = {Data processing, Mathematical statistics, Numerical analysis}
}
@book{gentleman_r_2008,
address = {Boca Raton},
edition = {1 edition},
title = {R {Programming} for {Bioinformatics}},
isbn = {978-1-4200-6367-7},
abstract = {Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems. Drawing on the author{\textquoteright}s first-hand experiences as an expert in R, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code. With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology.},
language = {English},
publisher = {Chapman and Hall/CRC},
author = {Gentleman, Robert},
month = jul,
year = {2008}
}
@book{stodden_implementing_2013,
title = {Implementing {Reproducible} {Research}},
urldate = {2015-12-30},
publisher = {Chapman \& Hall/CRC},
editor = {Stodden, Victoria and Leisch, Friedrich and Peng, Roger D.},
year = {2013},
keywords = {Electronic books, RESEARCH, Statistical methods, Reproducible research}
}
@article{dalgaard_introductory_2002,
series = {Statistics and computing},
title = {Introductory statistics with {R}},
url = {http://www2.lib.ku.edu/login?URL=http://site.ebrary.com/lib/kansas/Doc?id=10047812},
urldate = {2015-12-31},
journal = {ebrary Academic Complete},
author = {Dalgaard, Peter},
year = {2002},
keywords = {Data processing, Electronic books, R (Computer program language), Statistics}
}
@article{stodden_implementing_2013-1,
series = {Chapman \& {Hall}/{CRC} the {R} series},
title = {Implementing reproducible research},
journal = {Ebook library},
editor = {Stodden, Victoria and Leisch, Friedrich and Peng, Roger D.},
year = {2013},
keywords = {Electronic books, Statistical methods, Reproducible research, Research}
}
@book{levithan_regular_2012,
title = {Regular {Expressions} {Cookbook}},
isbn = {978-1-4493-1943-4},
year = {2012},
publisher = {O'Reilly Media},
url = {http://shop.oreilly.com/product/0636920023630.do},
abstract = {Take the guesswork out of using regular expressions. With more than 140 practical recipes, this cookbook provides everything you need to solve a wide range of real-world problems. Novices will learn basic skills and tools, and programmers and...},
language = {en},
urldate = {2018-05-11},
author = {Levithan, Steven, Jan Goyvaerts}
}
@book{xie_dynamic_2015,
address = {Boca Raton},
edition = {2 edition},
title = {Dynamic {Documents} with {R} and knitr, {Second} {Edition}},
isbn = {978-1-4987-1696-3},
abstract = {Quickly and Easily Write Dynamic Documents Suitable for both beginners and advanced users, Dynamic Documents with R and knitr, Second Edition makes writing statistical reports easier by integrating computing directly with reporting. Reports range from homework, projects, exams, books, blogs, and web pages to virtually any documents related to statistical graphics, computing, and data analysis. The book covers basic applications for beginners while guiding power users in understanding the extensibility of the knitr package. New to the Second Edition A new chapter that introduces R Markdown v2 Changes that reflect improvements in the knitr package New sections on generating tables, defining custom printing methods for objects in code chunks, the C/Fortran engines, the Stan engine, running engines in a persistent session, and starting a local server to serve dynamic documents Boost Your Productivity in Statistical Report Writing and Make Your Scientific Computing with R Reproducible Like its highly praised predecessor, this edition shows you how to improve your efficiency in writing reports. The book takes you from program output to publication-quality reports, helping you fine-tune every aspect of your report.},
language = {English},
publisher = {Chapman and Hall/CRC},
author = {Xie, Yihui},
month = jun,
year = {2015}
}
@book{xie_bookdown:_2016,
address = {Boca Raton, FL},
edition = {1 edition},
title = {bookdown: {Authoring} {Books} and {Technical} {Documents} with {R} {Markdown}},
isbn = {978-1-138-70010-9},
shorttitle = {bookdown},
abstract = {bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub.},
language = {English},
publisher = {Chapman and Hall/CRC},
author = {Xie, Yihui},
month = dec,
year = {2016}
}
@Book{mccullagh_nelder_1989,
Author = {McCullagh, P. and Nelder, John A.},
Title = {Generalized {Linear} {Models}, {Second} {Edition}},
Publisher = {Chapman and Hall/CRC},
Address = {Boca Raton},
Edition = {2 edition},
abstract = {The success of the first edition of Generalized Linear
Models led to the updated Second Edition, which
continues to provide a definitive unified, treatment of
methods for the analysis of diverse types of data.
Today, it remains popular for its clarity, richness of
content and direct relevance to agricultural,
biological, health, engineering, and other
applications.The authors focus on examining the way a
response variable depends on a combination of
explanatory variables, treatment, and classification
variables. They give particular emphasis to the
important case where the dependence occurs through some
unknown, linear combination of the explanatory
variables.The Second Edition includes topics added to
the core of the first edition, including conditional
and marginal likelihood methods, estimating equations,
and models for dispersion effects and components of
dispersion. The discussion of other topics-log-linear
and related models, log odds-ratio regression models,
multinomial response models, inverse linear and related
models, quasi-likelihood functions, and model
checking-was expanded and incorporates significant
revisions.Comprehension of the material requires simply
a knowledge of matrix theory and the basic ideas of
probability theory, but for the most part, the book is
self-contained. Therefore, with its worked examples,
plentiful exercises, and topics of direct use to
researchers in many disciplines, Generalized Linear
Models serves as ideal text, self-study guide, and
reference.},
isbn = {978-0-412-31760-6},
language = {English},
month = aug,
year = 1989
}
@Book{greene_econometric_2008,
Author = {Greene, William H.},
Title = {Econometric analysis},
Publisher = {Prentice Hall},
Edition = {6th ed},
isbn = {978-0-13-600383-0},
keywords = {Econometrics},
location = {Upper Saddle River, N.J},
pagetotal = {1178},
year = 2008
}
@Book{gelman_hill_2006,
Author = {Gelman, Andrew and Hill, Jennifer},
Title = {Data {Analysis} {Using} {Regression} and
{Multilevel}/{Hierarchical} {Models}},
Publisher = {Cambridge University Press},
Address = {Cambridge; New York},
Edition = {1 edition},
isbn = {978-0-521-68689-1},
language = {English},
location = {Cambridge},
year = 2006
}
@Article{bates_linear_2004,
Author = {Bates, Douglas M and DebRoy, Saikat},
Title = {Linear mixed models and penalized least squares},
Journal = {Journal of Multivariate Analysis},
Volume = {91},
Number = {1},
Pages = {1--17},
abstract = {Linear mixed-effects models are an important class of
statistical models that are used directly in many
fields of applications and also are used as iterative
steps in fitting other types of mixed-effects models,
such as generalized linear mixed models. The parameters
in these models are typically estimated by maximum
likelihood or restricted maximum likelihood. In
general, there is no closed-form solution for these
estimates and they must be determined by iterative
algorithms such as EM iterations or general nonlinear
optimization. Many of the intermediate calculations for
such iterations have been expressed as generalized
least squares problems. We show that an alternative
representation as a penalized least squares problem has
many advantageous computational properties including
the ability to evaluate explicitly a profiled
log-likelihood or log-restricted likelihood, the
gradient and Hessian of this profiled objective, and an
ECME update to refine this objective.},
doi = {10.1016/j.jmva.2004.04.013},
issn = {0047-259X},
keywords = {ECME algorithm, EM algorithm, Gradient, Hessian,
Maximum likelihood, Multilevel models, Profile
likelihood, REML},
month = oct,
series = {Special {Issue} on {Semiparametric} and
{Nonparametric} {Mixed} {Models}},
url = {http://www.sciencedirect.com/science/article/pii/S0047259X04000867},
urldate = {2016-02-06},
year = 2004
}
@Book{hastie_elements_2001,
Author = {Hastie, Trevor},
Title = {The elements of statistical learning: data mining,
inference, and prediction},
Publisher = {Springer},
Series = {Springer series in statistics},
Address = {New York},
collaborator = {Tibshirani, Robert and Friedman, J. H.},
isbn = {0-387-95284-5},
keywords = {Supervised learning (Machine learning)},
shorttitle = {The elements of statistical learning},
year = 2001
}
@Book{hastie_elements_2009,
Author = {Hastie, Trevor and Tibshirani, Robert and Friedman, J.
H},
Title = {The elements of statistical learning data mining,
inference, and prediction},
Publisher = {Springer},
Address = {New York},
abstract = {"During the past decade there has been an explosion in
computation and information technology. With it have
come vast amounts of data in a variety of fields such
as medicine, biology, finance, and marketing. The
challenge of understanding these data has led to the
development of new tools in the field of statistics,
and spawned new areas such as data mining, machine
learning, and bioinformatics. Many of these tools have
common underpinnings but are often expressed with
different terminology. This book describes the
important ideas in these areas in a common conceptual
framework. While the approach is statistical, the
emphasis is on concepts rather than mathematics. Many
examples are given, with a liberal use of color
graphics."--Jacket.},
isbn = {978-0-387-84858-7 0-387-84858-4 978-0-387-84857-0
0-387-84857-6},
language = {English},
year = 2009
}
@Book{lindsey_models_1999,
Author = {Lindsey, J. K.},
Title = {Models for {Repeated} {Measurements}},
Publisher = {Oxford University Press},
Address = {Oxford ; New York},
Edition = {2 edition},
abstract = {Models for Repeated Measurements will interest
research statisticians in agriculture, medicine,
economics, and psychology, as well as the many
consulting statisticians who want an up-to-date
expository account of this important topic. This
edition of this successful book has been completely
updated to take into account the many developments in
the area over the last few years. It features three new
chapters on models for continuous non-normal data, on
various design issues specific to repeated
measurements, and on missing data and dropouts.
Exercises have been added at the ends of most chapters,
and the software for carrying out the analyses is now
available to the public. The book begins with a
development of the general context of repeated
measurements. It then describes the three basic types
of response variables--continuous (normal),
categorical, and count data--and develops a practical
framework for creating suitable models and for applying
ideas on multivariate distributions and stochastic
processes. The book then devotes three sections to
examining a large number of concrete examples,
including data tables, to illustrate the models
available. The book also includes an extensive list of
references.},
isbn = {978-0-19-850559-4},
language = {English},
month = sep,
year = 1999
}
@Book{wood_generalized_2006,
Author = {Wood, Simon N},
Title = {Generalized {Additive} {Models}: {An} {Introduction}
with {R}},
Publisher = {Chapman \& Hall/CRC},
Address = {Boca Raton, FL},
isbn = {1-58488-474-6 978-1-58488-474-3},
language = {English},
shorttitle = {Generalized additive models},
year = 2006
}
@Book{pinheiro_bates_2000,
Author = {Pinheiro, Jos{\textbackslash}'\{e\} C. and Bates,
Douglas M.},
Title = {Mixed-{Effects} {Models} in {S} and {S}-{PLUS}},
Publisher = {Springer},
Series = {Statistics and computing},
Address = {New York},
isbn = {0-387-98957-9},
year = 2000
}
@Article{eilers_flexible_1996,
Author = {Eilers, Paul H. C. and Marx, Brian D.},
Title = {Flexible {Smoothing} with \${B}\$-splines and
{Penalties}},
Journal = {Statistical Science},
Volume = {11},
Number = {2},
Pages = {89--102},
abstract = {\$B\$-splines are attractive for nonparametric
modelling, but choosing the optimal number and
positions of knots is a complex task. Equidistant knots
can be used, but their small and discrete number allows
only limited control over smoothness and fit. We
propose to use a relatively large number of knots and a
difference penalty on coefficients of adjacent
\$B\$-splines. We show connections to the familiar
spline penalty on the integral of the squared second
derivative. A short overview of \$B\$-splines, of their
construction and of penalized likelihood is presented.
We discuss properties of penalized \$B\$-splines and
propose various criteria for the choice of an optimal
penalty parameter. Nonparametric logistic regression,
density estimation and scatterplot smoothing are used
as examples. Some details of the computations are
presented.},
issn = {0883-4237},
url = {http://www.jstor.org/stable/2246049},
urldate = {2016-06-01},
year = 1996
}
@Article{CroissantPLM,
title = {Panel Data Econometrics in {R}: The {plm} Package},
author = {Yves Croissant and Giovanni Millo},
journal = {Journal of Statistical Software},
year = {2008},
volume = {27},
number = {2},
url = {http://www.jstatsoft.org/v27/i02/},
}
@Article{Bateslme4,
title = {Fitting Linear Mixed-Effects Models Using {lme4}},
author = {Douglas Bates and Martin M{\"a}chler and Ben Bolker and Steve Walker},
journal = {Journal of Statistical Software},
year = {2015},
volume = {67},
number = {1},
pages = {1--48},
doi = {10.18637/jss.v067.i01},
}
@Article{WickhamReshape,
title = {Reshaping Data with the {reshape} Package},
author = {Hadley Wickham},
journal = {Journal of Statistical Software},
year = {2007},
volume = {21},
number = {12},
pages = {1--20},
url = {http://www.jstatsoft.org/v21/i12/},
}
@Article{WickhamPLYR,
title = {The Split-Apply-Combine Strategy for Data Analysis},
author = {Hadley Wickham},
journal = {Journal of Statistical Software},
year = {2011},
volume = {40},
number = {1},
pages = {1--29},
url = {http://www.jstatsoft.org/v40/i01/},
}
@Manual{DowleDataTable,
title = {data.table: Extension of Data.frame},
author = {M Dowle and A Srinivasan and T Short and S Lianoglou with contributions from R Saporta and E Antonyan},
year = {2015},
note = {R package version 1.9.6},
url = {https://CRAN.R-project.org/package=data.table},
}
@article{stodden_2013,
series = {Chapman \& {Hall}/{CRC} the {R} series},
title = {Implementing reproducible research},
journal = {Ebook library},
editor = {Stodden, Victoria and Leisch, Friedrich and Peng, Roger D.},
year = {2013},
keywords = {Electronic books, Statistical methods, Reproducible research, Research}
}