@comment{This file has been generated by Pybliographer}
@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 = {2 edition},
abstract = {The first edition of Analysis for Longitudinal Data
has become a classic. Describing the statistical models
and methods for the analysis of longitudinal data, it
covers both the underlying statistical theory of each
method, and its application to a range of examples from
the agricultural and biomedical sciences. The main
topics discussed are design issues, exploratory methods
of analysis, linear models for continuous data, general
linear models for discrete data, and models and methods
for handling data and missing values. Under each
heading, worked examples are presented in parallel with
the methodological development, and sufficient detail
is given to enable the reader to reproduce the author's
results using the data-sets as an appendix. This second
edition, published for the first time in paperback,
provides a thorough and expanded revision of this
important text. It includes two new chapters; the first
discusses fully parametric models for discrete repeated
measures data, and the second explores statistical
models for time-dependent predictors.},
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 Ltd},
Edition = {Second Edition},
abstract = {The Second Edition of this classic text introduces the
main methods, techniques, and issues involved in
carrying out multilevel modeling and analysis. Snijders
and Boskers? book is an applied, authoritative, and
accessible introduction to the topic, providing readers
with a clear conceptual and practical understanding of
all the main issues involved in designing multilevel
studies and conducting multilevel analysis. This book
has been comprehensively revised and updated since the
last edition, and now includes guides to modeling using
Hlm, {MlwiN}, Sas, Stata including Gllamm, R, Spss,
Mplus, {WinBugs}, Latent Gold, and Mix.},
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, S. and Skrondal, Anders},
Title = {Multilevel and longitudinal modeling using Stata},
Publisher = {Stata Press Publication},
Edition = {3rd ed},
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}, {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
}
@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 = {Third edition},
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, 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{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 ed},
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.},
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 = {1 edition},
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 = {2 edition},
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}, {Second} {Edition}},
Publisher = {Routledge},
Address = {New York},
Edition = {2 edition},
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},
}