% Encoding: UTF-8

@Manual{RCore,
  title        = {R: A language and environment for statistical computing},
  author       = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address      = {Vienna, Austria: R Foundation for Statistical Computing},
  year         = {2018},
  url          = {https://cran.r-project.org/doc/manuals/r-release/fullrefman.pdf},
}


@book{venablesripley2000,
	address = {New York},
	series = {Statistics and computing},
	title = {S {Programming}},
	isbn = {0-387-98966-8},
	publisher = {Springer},
	author = {Venables, William N. and 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{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{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 = {1st},
	title = {R {Programming} for {Bioinformatics}},
	isbn = {978-1-4200-6367-7},
	language = {English},
	publisher = {Chapman and Hall/CRC},
	author = {Gentleman, Robert},
	month = jul,
	year = {2008}
}

@book{levithan_regular_nodate,
	title = {Regular {Expressions} {Cookbook}},
	isbn = {978-1-4493-1943-4},
	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{fox_r_2011,
	address = {Thousand Oaks CA},
	edition = {2nd},
	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}
}


@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}
}




@Book{diggle_analysis_2013,
  Author         = {Diggle, Peter and Heagerty, Patrick and Liang,
                   Kung-Yee and Zeger, Scott},
  Title          = {Analysis of {Longitudinal} {Data}},
  Publisher      = {Oxford University Press},
  Address        = {Oxford},
  Edition        = {2nd},
  isbn           = {978-0-19-967675-0},
  language       = {English},
  month          = may,
  year           = 2013
}

@Book{snijders_multilevel_2011,
  Author         = {Snijders, Tom A. B. and Bosker, Roel},
  Title          = {Multilevel Analysis: An Introduction to Basic and
                   Advanced Multilevel Modeling},
  Publisher      = {{Sage} {Publications}},
  Edition        = {2nd},
  isbn           = {978-1-84920-201-5},
  location       = {Los Angeles, {CA}},
  pagetotal      = {368},
  shorttitle     = {Multilevel Analysis},
  year           = 2011
}

@Book{long_regression_1997,
  Author         = {Long, J. Scott},
  Title          = {Regression {Models} for {Categorical} and {Limited}
                   {Dependent} {Variables}},
  Publisher      = {Sage Publications},
  Number         = {7},
  Series         = {Advanced quantitative techniques in the social
                   sciences},
  Address        = {Thousand Oaks},
  isbn           = {0-8039-7374-8},
  keywords       = {Regression analysis},
  year           = 1997
}

@Book{singer_willett_2003,
  Author         = {Singer, Judith D. and Willett, John B.},
  Title          = {Applied longitudinal data analysis: modeling change
                   and event occurrence},
  Publisher      = {Oxford University Press},
  Address        = {Oxford ; New York},
  isbn           = {978-0-19-515296-8},
  keywords       = {Longitudinal method, RESEARCH, Social sciences},
  shorttitle     = {Applied longitudinal data analysis},
  year           = 2003
}

@Book{long_longitudinal_2011,
  Author         = {Long, Jeffrey D.},
  Title          = {Longitudinal {Data} {Analysis} for the {Behavioral}
                   {Sciences} {Using} {R}},
  Publisher      = {SAGE Publications, Inc},
  Address        = {Thousand Oaks, Calif},
  abstract       = {This book is unique in its focus on showing students
                   in the behavioral sciences how to analyze longitudinal
                   data using R software. The book focuses on application,
                   making it practical and accessible to students in
                   psychology, education, and related fields, who have a
                   basic foundation in statistics. It provides explicit
                   instructions in R computer programming throughout the
                   book, showing students exactly how a specific analysis
                   is carried out and how output is interpreted.},
  isbn           = {978-1-4129-8268-9},
  language       = {English},
  month          = oct,
  year           = 2011
}

@Book{rabe-hesketh_multilevel_2012,
  Author         = {{Rabe-Hesketh}, Sophia and Skrondal, Anders},
  Title          = {Multilevel and longitudinal modeling using Stata},
  Publisher      = {Stata Press Publication},
  Edition        = {3rd},
  isbn           = {978-1-59718-108-2 978-1-59718-103-7 978-1-59718-104-4},
  keywords       = {Data processing, Latent structure analysis, latent
                   variables, Linear models (Statistics), Mathematical
                   statistics, Multilevel models (Statistics), Stata},
  location       = {College Station, Tex},
  pagetotal      = {2},
  year           = 2012
}

@Book{raudenbush_hierarchical_2002,
  Author         = {Raudenbush, Stephen W. and Bryk, Anthony S.},
  Title          = {Hierarchical linear models: applications and data
                   analysis methods},
  Publisher      = {Sage},
  isbn           = {978-0-7619-1904-9},
  location       = {Thousand Oaks, {CA}},
  pagetotal      = {520},
  shorttitle     = {Hierarchical linear models},
  year           = 2002
}

@Book{mccullagh_nelder_1989,
  Author         = {McCullagh, P. and Nelder, John A.},
  Title          = {Generalized {Linear} {Models}},
  Publisher      = {Chapman and Hall/CRC},
  Address        = {Boca Raton},
  Edition        = {2nd},
  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},
  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        = {1st},
  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},
  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 and Tibshirani, Robert and Friedman, Jerome H},
  Title          = {The elements of statistical learning: data mining,
                   inference, and prediction},
  Publisher      = {Springer},
  Series         = {Springer series in statistics},
  Address        = {New York},
  isbn           = {0-387-95284-5},
  keywords       = {Supervised learning (Machine learning)},
  shorttitle     = {The elements of statistical learning},
  year           = 2001
}

@Article{fielding_2004,
  Author         = {{Antony Fielding}},
  Title          = {The {R}ole of the \{{H}\}ausman {T}est and whether
                   {H}igher {L}evel {E}ffects should be treated as
                   {R}andom or {F}ixed},
  Journal        = {Multilevel Modeling Newsletter},
  Volume         = {16},
  Number         = {2},
  Pages          = {3-9},
  year           = 2004
}

@Book{hsiao_analysis_2014,
  Author         = {Hsiao, Cheng},
  Title          = {Analysis of panel data},
  Publisher      = {Cambridge University Press},
  Number         = {54},
  Series         = {Econometric {Society} monographs},
  Address        = {New York, NY},
  Edition        = {3rd},
  Note           = {OCLC: ocn872561839},
  abstract       = {"This book provides a comprehensive, coherent, and
                   intuitive review of panel data methodologies that are
                   useful for empirical analysis. Substantially revised
                   from the second edition, it includes two new chapters
                   on modeling cross-sectionally dependent data and
                   dynamic systems of equations. Some of the more
                   complicated concepts have been further streamlined.
                   Other new material includes correlated random
                   coefficient models, pseudo-panels, duration and count
                   data models, quantile analysis, and alternative
                   approaches for controlling the impact of unobserved
                   heterogeneity in nonlinear panel data models"--},
  isbn           = {978-1-107-03869-1 978-1-107-65763-2},
  keywords       = {Analysis of Variance, Econometrics, Panel analysis},
  year           = 2014
}

@Book{hastie_elements_2009,
  Author         = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome 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        = {2nd},
  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{fitzmaurice_applied_2011,
  Author         = {Fitzmaurice, Garrett M. and Laird, Nan M. and Ware,
                   James H.},
  Title          = {Applied longitudinal analysis},
  Publisher      = {Wiley},
  Series         = {Wiley series in probability and statistics},
  Address        = {Hoboken, N.J},
  Edition        = {2nd},
  abstract       = {"Since the publication of the first edition, the
                   authors have solicited feedback from both the
                   instructors who use the book as a text for their
                   courses as well as the researchers who use the book as
                   a resource for their research. Thus, the improved
                   Second Edition of Applied Longitudinal Analysis
                   features many additions and revisions based on the
                   feedback of readers, making it the go-to reference for
                   applied use in public health, epidemiology, and
                   pharmaceutical sciences"--},
  isbn           = {978-0-470-38027-7},
  keywords       = {Longitudinal method, Medical statistics, Multivariate
                   analysis, Regression analysis},
  year           = 2011
}

@Book{mccullagh_nelder_1983,
  Author         = {McCullagh, P. and Nelder, John A.},
  Title          = {Generalized {Linear} {Models}},
  Publisher      = {Chapman and Hall},
  Number         = {37},
  Series         = {Monographs on statistics and applied probability},
  Address        = {London},
  collaborator   = {Nelder, John A},
  isbn           = {0-412-23850-0},
  keywords       = {Linear models (Statistics)},
  year           = 1983
}

@Article{king_statistical_1988,
  Author         = {King, Gary},
  Title          = {Statistical {Models} for {Political} {Science} {Event}
                   {Counts}: {Bias} in {Conventional} {Procedures} and
                   {Evidence} for the {Exponential} {Poisson} {Regression}
                   {Model}},
  Journal        = {American Journal of Political Science},
  Volume         = {32},
  Number         = {3},
  Pages          = {838--863},
  abstract       = {This paper presents analytical, Monte Carlo, and
                   empirical evidence on models for event count data.
                   Event counts are dependent variables that measure the
                   number of times some event occurs. Counts of
                   international events are probably the most common, but
                   numerous examples exist in every empirical field of the
                   discipline. The results of the analysis below strongly
                   suggest that the way event counts have been analyzed in
                   hundreds of important political science studies have
                   produced statistically and substantively unreliable
                   results. Misspecification, inefficiency, bias,
                   inconsistency, insufficiency, and other problems result
                   from the unknowing application of two common methods
                   that are without theoretical justification or empirical
                   utility in this type of data. I show that the
                   exponential Poisson regression (EPR) model provides
                   analytically, in large samples, and empirically, in
                   small, finite samples, a far superior model and optimal
                   estimator. I also demonstrate the advantage of this
                   methodology in an application to nineteenth-century
                   party switching in the U.S. Congress. Its use by
                   political scientists is strongly encouraged.},
  doi            = {10.2307/2111248},
  file           = {JSTOR Full Text
                   PDF:/home/pauljohn/.mozilla/firefox/mx4cgvoq.default-1391919321660/zotero/storage/G5GEHHWM/King
                   - 1988 - Statistical Models for Political Science Event
                   Cou.pdf:application/pdf},
  issn           = {0092-5853},
  shorttitle     = {Statistical {Models} for {Political} {Science} {Event}
                   {Counts}},
  url            = {http://www.jstor.org/stable/2111248},
  urldate        = {2011-08-18},
  year           = 1988
}

@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
}

@InCollection{snijders_berkof_2008,
  Author         = {{T.A.B. Snijders and J. Berkof}},
  Title          = {Diagnostic checks for multilevel models},
  BookTitle      = {Handbook of {M}ultilevel {A}nalysis},
  Publisher      = {New York: Springer},
  Pages          = {141-175},
  year           = 2008
}

@Book{hox_handbook_2010,
  Editor         = {Hox, Joop and Roberts, J. Kyle},
  Title          = {Handbook of {Advanced} {Multilevel} {Analysis}},
  Publisher      = {Routledge},
  Address        = {New York},
  Edition        = {1st},
  abstract       = {This new handbook is the definitive resource on
                   advanced topics related to multilevel analysis. The
                   editors assembled the top minds in the field to address
                   the latest applications of multilevel modeling as well
                   as the specific difficulties and methodological
                   problems that are becoming more common as more
                   complicated models are developed. Each chapter features
                   examples that use actual datasets. These datasets, as
                   well as the code to run the models, are available on
                   the book's website http://www.hlm-online.com . Each
                   chapter includes an introduction that sets the stage
                   for the material to come and a conclusion. Divided into
                   five sections, the first provides a broad introduction
                   to the field that serves as a framework for
                   understanding the latter chapters. Part 2 focuses on
                   multilevel latent variable modeling including item
                   response theory and mixture modeling. Section 3
                   addresses models used for longitudinal data including
                   growth curve and structural equation modeling. Special
                   estimation problems are examined in section 4 including
                   the difficulties involved in estimating survival
                   analysis, Bayesian estimation, bootstrapping, multiple
                   imputation, and complicated models, including
                   generalized linear models, optimal design in multilevel
                   models, and more. The book's concluding section
                   focuses on statistical design issues encountered when
                   doing multilevel modeling including nested designs,
                   analyzing cross-classified models, and dyadic data
                   analysis. Intended for methodologists, statisticians,
                   and researchers in a variety of fields including
                   psychology, education, and the social and health
                   sciences, this handbook also serves as an excellent
                   text for graduate and PhD level courses in multilevel
                   modeling. A basic knowledge of multilevel modeling is
                   assumed.},
  isbn           = {978-1-84169-722-2},
  language       = {English},
  month          = jul,
  year           = 2010
}

@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
}

@Book{cameron_regression_2013,
  Author         = {Cameron, A. Colin and Trivedi, Pravin K.},
  Title          = {Regression {Analysis} of {Count} {Data}},
  Publisher      = {Cambridge University Press},
  Address        = {Cambridge ; New York, NY},
  Edition        = {2nd},
  abstract       = {Students in both social and natural sciences often
                   seek regression methods to explain the frequency of
                   events, such as visits to a doctor, auto accidents, or
                   new patents awarded. This book provides the most
                   comprehensive and up-to-date account of models and
                   methods to interpret such data. The authors have
                   conducted research in the field for more than
                   twenty-five years. In this book, they combine theory
                   and practice to make sophisticated methods of analysis
                   accessible to researchers and practitioners working
                   with widely different types of data and software in
                   areas such as applied statistics, econometrics,
                   marketing, operations research, actuarial studies,
                   demography, biostatistics, and quantitative social
                   sciences. The book may be used as a reference work on
                   count models or by students seeking an authoritative
                   overview. Complementary material in the form of data
                   sets, template programs, and bibliographic resources
                   can be accessed on the Internet through the authors'
                   homepages. This second edition is an expanded and
                   updated version of the first, with new empirical
                   examples and more than one hundred new references
                   added. The new material includes new theoretical
                   topics, an updated and expanded treatment of
                   cross-section models, coverage of bootstrap-based and
                   simulation-based inference, expanded treatment of time
                   series, multivariate and panel data, expanded treatment
                   of endogenous regressors, coverage of quantile count
                   regression, and a new chapter on Bayesian methods.},
  isbn           = {978-1-107-66727-3},
  language       = {English},
  month          = may,
  year           = 2013
}

@Book{hox_multilevel_2010,
  Author         = {Hox, Joop J. and Moerbeek, Mirjam and Schoot, Rens van
                   de},
  Title          = {Multilevel {Analysis}: {Techniques} and
                   {Applications}},
  Publisher      = {Routledge},
  Address        = {New York},
  Edition        = {2nd},
  abstract       = {This practical introduction helps readers apply
                   multilevel techniques to their research. Noted as an
                   accessible introduction, the book also includes
                   advanced extensions, making it useful as both an
                   introduction and as a reference to students,
                   researchers, and methodologists. Basic models and
                   examples are discussed in non-technical terms with an
                   emphasis on understanding the methodological and
                   statistical issues involved in using these models. The
                   estimation and interpretation of multilevel models is
                   demonstrated using realistic examples from various
                   disciplines. For example, readers will find data sets
                   on stress in hospitals, GPA scores, survey responses,
                   street safety, epilepsy, divorce, and sociometric
                   scores, to name a few. The data sets are available on
                   the website in SPSS, HLM, MLwiN, LISREL and/or Mplus
                   files. Readers are introduced to both the multilevel
                   regression model and multilevel structural models.
                   Highlights of the second edition include: Two new
                   chapters-one on multilevel models for ordinal and
                   count data (Ch. 7) and another on multilevel survival
                   analysis (Ch. 8). Thoroughly updated chapters on
                   multilevel structural equation modeling that reflect
                   the enormous technical progress of the last few years.
                   The addition of some simpler examples to help the
                   novice, whilst the more complex examples that combine
                   more than one problem have been retained. A new section
                   on multivariate meta-analysis (Ch. 11). Expanded
                   discussions of covariance structures across time and
                   analyzing longitudinal data where no trend is expected.
                   Expanded chapter on the logistic model for dichotomous
                   data and proportions with new estimation methods. An
                   updated website at http://www.joophox.net/ with data
                   sets for all the text examples and up-to-date screen
                   shots and PowerPoint slides for instructors. Ideal for
                   introductory courses on multilevel modeling and/or ones
                   that introduce this topic in some detail taught in a
                   variety of disciplines including: psychology,
                   education, sociology, the health sciences, and
                   business. The advanced extensions also make this a
                   favorite resource for researchers and methodologists in
                   these disciplines. A basic understanding of ANOVA and
                   multiple regression is assumed. The section on
                   multilevel structural equation models assumes a basic
                   understanding of SEM.},
  isbn           = {978-1-84872-846-2},
  language       = {English},
  month          = apr,
  shorttitle     = {Multilevel {Analysis}},
  year           = 2010
}

@Book{cameron_regression_1998,
  Author         = {Cameron, Adrian Colin},
  Title          = {Regression {Analysis} of {Count} {Data}},
  Publisher      = {Cambridge University Press},
  Number         = {no. 30},
  Series         = {Econometric {Society} monographs},
  Address        = {Cambridge ; New York},
  collaborator   = {Trivedi, P. K.},
  isbn           = {0-521-63201-3},
  keywords       = {Econometrics, Regression analysis},
  year           = 1998
}

@Article{greene_2011,
  Author         = {{William Greene}},
  Title          = {Fixed {E}ffects {V}ector {D}ecomposition: {A}
                   {M}agical {S}olution to the {P}roblem of
                   {T}ime-{I}nvariant {V}ariables in {F}ixed {E}ffects
                   {M}odels},
  Journal        = {Political Analysis},
  Volume         = {19},
  Pages          = {135--146},
  year           = 2011
}

@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},
  }

@Book{stodden_implementing_2014,
  title     = {Implementing reproducible research},
  publisher = {CRC},
  year      = {2014},
  editor    = {Stodden, Victoria and Leisch, Friedrich and Peng, Roger D.},
  address   = {Boca Raton, FL},
  month     = apr,
  isbn      = {978-1-4665-6159-5},
  abstract  = {In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result.   Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes:   Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research Software and methodological platforms, including open source software packages, RunMyCode platform, and open access journals  Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org.},
  language  = {English},
}

@Book{xie_dynamic_2015,
  title     = {Dynamic Documents with {R} and knitr},
  publisher = {CRC},
  year      = {2015},
  author    = {Xie, Yihui},
  address   = {Boca Raton, FL},
  edition   = {2nd},
  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},
}

@Book{xie_bookdown:_2016,
  title      = {bookdown: {A}uthoring books and technical documents with {R} {M}arkdown},
  publisher  = {CRC},
  year       = {2016},
  author     = {Xie, Yihui},
  address    = {Boca Raton, FL},
  month      = dec,
  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},
  shorttitle = {bookdown},
}

@Manual{xie_knitr,
  title  = {knitr: A general-purpose package for dynamic report generation in {R}},
  author = {Yihui Xie},
  year   = {2018},
  note = {R package version 1.20},
  url    = {https://yihui.name/knitr},
}

@Manual{Allaire_rmarkdown,
  title  = {rmarkdown: Dynamic documents for {R}},
  author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Aidan Lister},
  year   = {2018},
  note = {R package version 1.10},
  url    = {https://cran.r-project.org/web/packages/rmarkdown/rmarkdown.pdf},
}


@book{knuth_art_1968,
	address = {Reading, Mass},
	series = {Addison-{Wesley} series in computer science and information processing},
	title = {The art of computer programming},
	publisher = {Addison-Wesley Pub. Co},
	author = {Knuth, Donald Ervin},
	year = {1968},
	note = {OCLC: 00474887},
	keywords = {Computer programming}
}

@Book{knuth_texbook_1984,
  title     = {The {TeXbook}},
  publisher = {Addison-Wesley},
  year      = {1984},
  author    = {Knuth, Donald Ervin},
  address   = {Reading, MA},
  keywords  = {Computerized typesetting, Mathematics printing, TeX (Computer file)},
}

@Article{knuth_literate_1984,
  author  = {Knuth, D. E.},
  title   = {Literate programming},
  journal = {The Computer Journal},
  year    = {1984},
  volume  = {27},
  pages   = {97--111},
  doi     = {10.1093/comjnl/27.2.97},
}

@book{Dalgaard2002,
	address = {New York},
	series = {Statistics and computing},
	title = {Introductory statistics with {R}},
	isbn = {978-0-387-95475-2},
	publisher = {Springer},
	author = {Dalgaard, Peter},
	year = {2002},
	note = {OCLC: ocm49386016},
	keywords = {Data processing, R (Computer program language), Statistics}
}

@InCollection{leisch2002,
  author    = {Friedrich Leisch},
  title     = {Sweave: Dynamic generation of statistical reports using literate data analysis},
  booktitle = {Compstat 2002: Proceedings in the 15th {C}omputational {S}tatistics {S}ymposium in {B}erlin},
  publisher = {Physica-Verlag Heidelberg},
  year      = {2002},
  editor    = {W. Hardle and B. Ronz},
  pages     = {575--580},
  address   = {New York},
}

@Manual{MacFarlane,
  title  = {Pandoc user's guide},
  author = {John MacFarlane},
  year   = {2018},
  url    = {https://pandoc.org/MANUAL.pdf},
}

@Manual{bootstrap,
  title    = {Bootstrap},
  author   = {Jacob Thornton and Mark Otto},
  year     = {2018},
  abstract = {The most popular HTML, CSS, and JS library in the world.},
  language = {en},
  url      = {https://getbootstrap.com/},
}

@Manual{Tantau2016,
  title  = {The {BEAMER} class: User guide for {V}ersion 3.50},
  author = {Till Tantau and Joseph Wright and Vedran Miletic},
  year   = {2016},
  url    = {http://www.ctan.org/tex-archive/macros/latex/contrib/beamer/doc/beameruserguide.pdf},
}

@Comment{jabref-meta: databaseType:bibtex;}
