  %\VignetteIndexEntry{texreg: Conversion of Statistical Model Output in R to LaTeX and HTML tables}
%\VignetteDepends{}
%\VignetteKeyword{typesetting, reporting, table, coefficients, regression, R, LaTeX, MS Word, HTML, Markdown}
%\VignettePackage{texreg}

\documentclass[nojss]{jss}
\usepackage{booktabs,dcolumn,rotating,thumbpdf,longtable,paralist}

\usepackage{tikz}
\usetikzlibrary{trees}

\graphicspath{{Figures/}}

\author{Philip Leifeld \\ University of Essex}
\Plainauthor{Philip Leifeld}

\title{\pkg{texreg}: Conversion of Statistical Model Output in \proglang{R} to {\LaTeX} and HTML Tables}
\Plaintitle{texreg: Conversion of Statistical Model Output in R to LaTeX and HTML Tables}
\Shorttitle{\pkg{texreg}: Conversion of \proglang{R} Model Output to {\LaTeX} and HTML}

\Abstract{ A recurrent task in applied statistics is the (mostly
  manual) preparation of model output for inclusion in
  {\LaTeX}, Microsoft Word, or HTML
  documents -- usually with more than one model presented in a single
  table along with several goodness-of-fit statistics.  However,
  statistical models in \proglang{R} have diverse object structures
  and summary methods, which makes this process cumbersome.  This
  article first develops a set of guidelines for converting
  statistical model output to {\LaTeX} and HTML
  tables, then assesses to what extent existing packages meet these
  requirements, and finally presents the \pkg{texreg} package as a
  solution that meets all of the criteria set out in the beginning.
  After providing various usage examples, a blueprint for writing
  custom model extensions is proposed.
}

\Keywords{reporting, table, coefficients, regression, \proglang{R}, {\LaTeX}, MS Word, HTML, Markdown}
\Plainkeywords{reporting, table, coefficients, regression, R, LaTeX, MS Word, HTML, Markdown}

\Volume{55}
\Issue{8}
\Month{November}
\Year{2013}
\Submitdate{2012-10-16}
\Acceptdate{2013-04-04}

\Address{
  Philip Leifeld\\
  Department of Government\\
  University of Essex\\
  Wivenhoe Park\\
  Colchester, CO4~6BW\\
  United Kingdom\\
  E-mail: \email{philip.leifeld@essex.ac.uk}\\
  URL: \url{https://www.philipleifeld.com}
}


\begin{document}
\SweaveOpts{concordance=TRUE}

This \proglang{R} package vignette is based on an article in the \emph{Journal of Statistical Software} \citep{leifeld2013texreg:}. The contents of the article have not been modified, apart from the author affiliation. However, the package has been updated with more arguments, model extensions, and functions. The help pages contain details on any differences to the version documented here.

\section[Typesetting R model output in LaTeX and HTML]{Typesetting \proglang{R} model output in {\LaTeX} and HTML}

The primary purpose of the statistical programming language
\proglang{R} \citep{rcoreteam2012r} is the analysis of data with
statistical models.  One of the strengths of \proglang{R} is that
users can implement their own statistical models.  While this
flexibility leads to an increased availability of even exotic models
and shorter cycles between model development and implementation, there
are also downsides of this flexibility.  In particular, there is no
unified data structure of statistical models and no unified way of
representing model output, which makes it hard to re-use coefficients
and goodness-of-fit (GOF) statistics in other software packages, especially
for the purpose of publishing results.

Several generic functions were developed to provide unified accessor
methods for coefficients (the \code{coef()} function), GOF statistics
(for example, \code{AIC()}, \code{BIC()}, \code{logLik()}, or
\code{deviance()}), a custom text representation of the fitted model
(\code{summary()}), and other relevant pieces of information (e.g.,
\code{nobs()} and \code{formula()}).  Details are provided in
Chapter~11 of \citet{venables2012introduction}.  Nonetheless, many
popular packages have only partially implemented methods for these
generics, and in some cases they do not even provide accessor
functions at all for their coefficients or GOF statistics.  Even
worse, the model summary methods are usually structured in
idiosyncratic ways and do not lend themselves to easy parsing of
coefficients and GOF statistics.

Modern scientific journals, on the other hand, often require nicely
formatted and standardized model output, usually in the form of
coefficient tables for one or more models.  In the majority of
applications, these tables show more than one model aligned next to
each other with partially overlapping coefficient names, standard
errors in parentheses, and superscripted stars indicating the
significance of model terms.  At the bottom of the table, summary
statistics like the number of observations are reported, and
GOF measures like AIC or R$^2$ are shown.  Due to the
idiosyncratic way model output is currently represented in various
classes in \proglang{R}, designing these kinds of tables for a paper
submission requires a substantial amount of time and patience,
especially if more than one model is involved and if there are many
model terms.  Copying and pasting coefficients and standard errors one
at a time often becomes the default way of handling this task.

An important tool for typesetting academic papers in many academic
fields is {\LaTeX} \citep{lamport1986latex}.  In fact,
\proglang{R} and {\LaTeX} are closely linked by the
\code{Sweave()} command in the \pkg{utils} package
\citep{rcoreteam2012r}, which allows the integration of \proglang{R}
commands in {\LaTeX} documents and their execution and
evaluation at runtime \citep{leisch2002sweave}.  In spite of this,
common approaches for linking \proglang{R} model output and tables in
{\LaTeX} include
\begin{inparaenum}[(1)]
\item copying and pasting individual values after every change of the
  model,
\item custom user-written functions which convert a specific model
  into a matrix,
\item the use of sophisticated table-management packages (see next
  section), and
\item the inclusion of single models in the form of the model summary
  instead of nicely aligned coefficient tables as a second best
  solution.
\end{inparaenum}

Popular alternatives for document preparation include
\proglang{Microsoft Word} and the dynamic report generation
\proglang{R} package \pkg{knitr} \citep{knitr1, knitr2, xie2012knitr}.
Both \pkg{knitr} and Microsoft Word accept HTML input, and
\pkg{knitr} additionally supports Markdown, a simplified
HTML-like markup language.  These platforms face similar
complications as {\LaTeX} and \code{Sweave()} regarding the preparation
of regression tables for multiple statistical models.

The ideal way to prepare \proglang{R} model output for
{\LaTeX} and HTML tables would be a generic
function which would directly output {\LaTeX} or
HTML tables and for which custom methods for any model type
could be written as extensions.  While several attempts already exist
(see Section~\ref{comparison}), all of them have limitations.  This
article introduces the \pkg{texreg} package \citep{Leifeld:2013}, which
closes this gap and provides a unified framework for typesetting
{\LaTeX} and HTML tables for various statistical
models in \proglang{R}. Package \pkg{texreg} is available from
the Comprehensive \proglang{R} Archive Network (CRAN) at
\url{https://CRAN.R-project.org/package=texreg}.

The remainder of this article is structured as follows:
Section~\ref{requirements} sets out a number of requirements which
must be met.  In the light of these requirements,
Section~\ref{comparison} compares \pkg{texreg} to other \proglang{R}
packages and functions which were designed for similar purposes.
Section~\ref{description} describes the way how \pkg{texreg} works and
how its functions and classes are related.  After providing several
examples and illustrating the options of the \code{texreg()},
\code{htmlreg()}, and \code{screenreg()} functions
(Section~\ref{examples}), Section~\ref{extensions} describes how new
extensions can be implemented.


\section{Requirements} \label{requirements}

The design of the \pkg{texreg} package tries to accomplish six goals:
it should be capable of dealing with several models in a single table;
it should be easily extensible by package writers and users; it should
provide options for using the available space in an optimal way; it
should take advantage of advanced layout capabilities in
{\LaTeX} and HTML; it should take care of journal-
or model-specific habits or best practices; and it should find an
optimal balance of customizability and usability.  These requirements
are elaborated in the following paragraphs.

\subsection{Managing multiple models}
Quite often, almost-identical models are printed in order to show how
an additional model term alters the other coefficients and standard
errors.  There are, however, also cases where different model types
are applied to the same data.  This implies that the package must not
only be able to merge the names of coefficients to guarantee
comparability of coefficient columns; it must also be able to deal
with different model classes and accommodate different kinds of
GOF statistics in the same table.

Moreover, it must be possible to rename the model terms and
GOF statistics.  Custom coefficient names not only make
the output more easily comprehensibe by the readers; renaming model
terms is also mandatory for unifying terms between several models.
For example, two models based upon two different datasets may have
different variable names for the same kind of theoretical construct.
This would result in two separate but complementary rows in the
coefficient table.  It should be possible to rename coefficients and
then conflate any two or more complementary rows with identical
labels.

Finally, it should be possible to assign custom names for the
different models, instead of the default ``Model 1'', ``Model 2'',
etc.  While it may be easy to rename them manually in many
applications, particularly \code{Sweave()} and \code{knitr()} require
that no manual manipulation is necessary.

\subsection[Using generics to make texreg easily extensible]{Using generics to make \pkg{texreg} easily extensible}
Different model classes have different ways how their coefficients and
GOF statistics can be accessed.  Hardcoding these
extraction rules into the functions of the \pkg{texreg} package would
inhibit customizability.  The best way to make \pkg{texreg} extensible
is to have a generic \code{extract()} function which can be invoked on
any kind of model, just like \code{plot()} or \code{print()} generics
in \proglang{R}.  Any user---especially model class authors---would
then be able to add custom methods to the \code{extract()} function in
order to make \pkg{texreg} learn how to cope with new models.  For
example, an \code{extract()} method for `\code{lm}' objects can be
written to deal with linear models, or an \code{extract()} method for
`\code{ergm}' objects can be written to make the generic
\code{extract()} function understand exponential random graph models.
All the user has to do is write a custom extract function for a
specific model type and then register it as a method for the generic
\code{extract()} function.  Section~\ref{extensions} provides details
on how this can be accomplished.

\subsection{Use available space efficiently} \label{space} If a table
has many model terms and if standard errors are printed in parentheses
below the coefficients, the table may become too large for a single
page.  For this reason, it should be possible to print standard errors
right beside the coefficients instead of aligning them vertically.  In
\pkg{texreg}, this is achieved with the \code{single.row} argument.

If tables grow too large, other measures might prove useful: removing
table margins, setting the table in script size, or setting custom
float positions (for {\LaTeX} tables).  Very wide tables should be
rotated by 90 degrees using the \code{sidewaystable} environment in
the {\LaTeX} package \pkg{rotating} \citep{rahtz2008rotating} in order
to use the available space in an optimal way.  The user should also be
able to set the table caption and label, decide whether the table
should be in a float environment (for {\LaTeX} tables), align the table
horizontally on the page, and set the position of the caption.  The
\pkg{texreg} package provides arguments to control all of these
aspects.

\subsection{Beautiful and clean table layout} \label{tablayout} For
{\LaTeX} tables, the {\LaTeX} package \pkg{dcolumn}
\citep{carlisle2001dcolumn} provides facilities for aligning numbers
in table cells nicely, for example at their decimal separators.  The
\pkg{booktabs} package \citep{fear2005booktabs} can draw top, mid and
bottom rules for tables and produces a cleaner layout than the default
horizontal lines.  Both packages are supported by \pkg{texreg} and can
be switched on or off depending on the availability of the packages in
the {\LaTeX} distribution.

For HTML tables, cascading style sheets (CSS) should be used to adjust
the layout, and the user should be able to decide whether CSS markup
should be included in the file header or inline.

\subsection{Journal- or model-specific requirements} \label{specific}
Academic journals may have different requirements regarding the number
of digits to be printed, the inclusion of superscripted stars
indicating significance, or the removal of leading zeroes.  Similarly,
there are best practices in different academic communities or for
specific model types.  For example, it is common practice to attach
three stars to coefficients with $p$~values $\leq 0.001$ and small
centered dots to coefficients with $p$~values between $0.05$ and $0.1$
in exponential random graph models, while less fine-grained
significance levels are adopted in many other communities (for
example, three stars for $p \leq 0.01$, or only one star or bold
formatting for one single significance level).  In yet other
communities, journals or models, $p$~values or significance stars are
not required or even deemed inappropriate \citep[see the \pkg{lme4}
package by][]{bates2013lme4:}.

\subsection{Customizability and usability} \label{customizability}
Different users have different applications in mind.
For this reason, a solution should be as flexible as possible and offer
customization via arguments.  For example, inclusion of an HTML table
in a \pkg{knitr} Markdown document requires that only the table is
printed without any header or document type information, and that
significance stars are escaped using backslashes.

In other situations, it may be important to
\begin{inparaenum}[(1)]
\item omit certain coefficients (like random or fixed effects or
  thresholds),
\item reorder the coefficients in the model (e.g., because some models
  put interaction effects at the end of the list of coefficients), or
\item replace coefficients, standard errors, or $p$~values by custom
  vectors, for example when heteroskedasticity-consistent (``robust'')
  standard errors are used \citep{zeileis2004econometric}.
\end{inparaenum}

\pagebreak

\LTcapwidth=\textwidth
\begin{center}
\begin{longtable}{l l l l l}
\toprule
Argument			& \rotatebox{90}{\code{texreg}}	& \rotatebox{90}{\code{htmlreg}}	& \rotatebox{90}{\code{screenreg}}	& Short description \\
\midrule
\endfirsthead

\multicolumn{5}{r}%
{{\tablename\ \thetable{} -- continued from previous page}} \\
\toprule
Argument			& \rotatebox{90}{\code{texreg}}	& \rotatebox{90}{\code{htmlreg}}	& \rotatebox{90}{\code{screenreg}}	& Short description \\
\midrule
\endhead

\midrule
\multicolumn{5}{r}{{Continued on next page}} \\
\bottomrule
\endfoot

\caption[Arguments of the texreg(), htmlreg() and screenreg() functions.]{\label{tab:arguments} Arguments of the \code{texreg()}, \code{htmlreg()} and \code{screenreg()} functions.} \\
\endlastfoot

\code{l}			& $\bullet$	& $\bullet$	& $\bullet$	& Model or list of models \\
\code{file}			& $\bullet$	& $\bullet$	& $\bullet$	& Divert output to a file \\
\code{single.row}		& $\bullet$	& $\bullet$	& $\bullet$	& Print coefs and standard errors in the same row? \\
\code{stars}			& $\bullet$	& $\bullet$	& $\bullet$	& Threshold levels for significance stars \\
\code{custom.model.names}	& $\bullet$	& $\bullet$	& $\bullet$	& Set the names of the models \\
\code{custom.coef.names}	& $\bullet$	& $\bullet$	& $\bullet$	& Replace the names of the model terms \\
\code{custom.gof.names}		& $\bullet$	& $\bullet$	& $\bullet$	& Replace the names of the GOF statistics \\
\code{custom.note}		& $\bullet$	& $\bullet$	& $\bullet$	& Replace the default significance legend \\
\code{digits}			& $\bullet$	& $\bullet$	& $\bullet$	& Number of decimal places \\
\code{leading.zero}		& $\bullet$	& $\bullet$	& $\bullet$	& Print leading zeroes? \\
\code{symbol}			& $\bullet$	& $\bullet$	& $\bullet$	& Dot symbol denoting a fourth significance level \\
\code{override.coef}		& $\bullet$	& $\bullet$	& $\bullet$	& Replace coefficients by custom vectors \\
\code{override.se}		& $\bullet$	& $\bullet$	& $\bullet$	& Replace standard errors by custom vectors \\
\code{override.pval}		& $\bullet$	& $\bullet$	& $\bullet$	& Replace $p$~values by custom vectors \\
\code{omit.coef}		& $\bullet$	& $\bullet$	& $\bullet$	& Remove rows using a regular expression \\
\code{reorder.coef}		& $\bullet$	& $\bullet$	& $\bullet$	& Provide a custom order for the model terms \\
\code{reorder.gof}		& $\bullet$	& $\bullet$	& $\bullet$	& Provide a custom order for the GOF statistics \\
\code{return.string}		& $\bullet$	& $\bullet$	& $\bullet$	& Return the table as a character vector? \\
\code{ci.force}			& $\bullet$	& $\bullet$	& $\bullet$	& Convert standard errors to confidence intervals \\
\code{ci.level}			& $\bullet$	& $\bullet$	& $\bullet$	& Confidence level for CI conversion \\
\code{ci.star}			& $\bullet$	& $\bullet$	& $\bullet$	& Print star when 0 is not contained in the CI \\
\code{bold}			& $\bullet$	& $\bullet$	& $\circ$	& $p$~value below which coefficients are bolded \\
\code{center}			& $\bullet$	& $\bullet$	& $\circ$	& Horizontal alignment on the page \\
\code{caption}			& $\bullet$	& $\bullet$	& $\circ$	& Set the caption of the table \\
\code{caption.above}		& $\bullet$	& $\bullet$	& $\circ$	& Should the caption be placed above the table? \\
\code{label}			& $\bullet$	& $\circ$	& $\circ$	& Set the label of the table \\
\code{booktabs}			& $\bullet$	& $\circ$	& $\circ$	& Use the \pkg{booktabs} package \citep{fear2005booktabs}? \\
\code{dcolumn}			& $\bullet$	& $\circ$	& $\circ$	& Use the \pkg{dcolumn} package \citep{carlisle2001dcolumn}? \\
\code{sideways}			& $\bullet$	& $\circ$	& $\circ$	& Use \code{sidewaystable} \citep{rahtz2008rotating}? \\
\code{use.packages}		& $\bullet$	& $\circ$	& $\circ$	& Print the \verb+\usepackage{}+ declarations? \\
\code{table}			& $\bullet$	& $\circ$	& $\circ$	& Wrap \code{tabular} in a table environment? \\
\code{no.margin}		& $\bullet$	& $\circ$	& $\circ$	& Remove margins between columns to save space \\
\code{scriptsize}		& $\bullet$	& $\circ$	& $\circ$	& Use smaller font size to save space \\
\code{float.pos}		& $\bullet$	& $\circ$	& $\circ$	& Specify floating position of the table \\
\code{star.symbol}		& $\circ$	& $\bullet$	& $\circ$	& Change the significance symbol \\
\code{inline.css}		& $\circ$	& $\bullet$	& $\circ$	& Use CSS in the text rather than the header \\
\code{doctype}			& $\circ$	& $\bullet$	& $\circ$	& Include the \code{DOCTYPE} declaration? \\
\code{html.tag}			& $\circ$	& $\bullet$	& $\circ$	& Include the \code{<html>} tag? \\
\code{head.tag}			& $\circ$	& $\bullet$	& $\circ$	& Include the \code{<head>} tag? \\
\code{body.tag}			& $\circ$	& $\bullet$	& $\circ$	& Include the \code{<body>} tag? \\
\code{column.spacing}		& $\circ$	& $\circ$	& $\bullet$	& Number of spaces between columns \\
\code{outer.rule}		& $\circ$	& $\circ$	& $\bullet$	& Line type for the outer rule \\
\code{inner.rule}		& $\circ$	& $\circ$	& $\bullet$	& Line type for the inner rule \\
\code{...}			& $\bullet$	& $\bullet$	& $\bullet$	& Additional arguments for the extract functions \\
\bottomrule\\
\end{longtable}
\end{center}

\vspace*{-1.0cm}

On the other hand, users should not be required to learn the meaning
of all arguments before they can typeset their first table.  The
default arguments should serve the needs of occasional users.
Moreover, adjusting tables based on a complex set of arguments should
be facilitated by printing tables to the \proglang{R} console before
actually generating the {\LaTeX} or HTML output.
If this screen representation of the table is nicely formatted and
aligned using spaces and rules, it can also serve as an occasional
replacement for the generic \code{summary()} method for easy model
comparison as part of the statistical modeling workflow.

The \pkg{texreg} package tries to balance these needs for
customizability and usability by providing many arguments for layout
customization (see Table~\ref{tab:arguments} for a list of arguments),
using sensible default values for occasional users, and providing a
function for on-screen display of tables for easy model comparison and
layout adjustment.


\section{Comparison with other packages} \label{comparison}

Beside \pkg{texreg}, several other packages were designed to convert
\proglang{R} model output to {\LaTeX} or HTML
tables.

The \pkg{xtable} package \citep{dahl2012xtable} is able to typeset
various matrices and data frames from \proglang{R} as
{\LaTeX} or HTML tables.  It is very flexible and
has its strengths particularly when it comes to tables of summary
statistics.  However, it was not specifically designed for statistical
model output.  Similarly, the \code{mat2tex()} command from the
\pkg{sfsmisc} package \citep{maechler2012sfsmisc} can export matrices
to {\LaTeX}, and the \code{tex.table()} function in the
\pkg{cwhmisc} package \citep{hoffmann2012cwhmisc} is able to export
data frames as {\LaTeX} tables.

For several years, the \code{outreg()} function in the \pkg{rockchalk}
package \citep{johnson2012rockchalk} has been available for exporting
multiple regression models to {\LaTeX} .  However, the
function remains fairly basic and does not provide a great deal of
layout options, generics, and custom model types (in fact, only
`\code{lm}' and `\code{glm}' objects).

The \pkg{apsrtable} package \citep{malecki2012apsrtable}, the
\code{mtable()} function from the \pkg{memisc} package
\citep{elff2012memisc}, and the \pkg{stargazer} package
\citep{hlavac2013stargazer} are more advanced and can also merge
multiple models in a single table.  \pkg{apsrtable} and \pkg{memisc}
feature custom functions for the extraction of coefficient and
GOF information, and they are based on generics.  In this
regard, both packages are somewhat similar to the \pkg{texreg}
package.  \pkg{texreg}, however, offers more straightforward ways of
custom model implementation.  This important feature is notably absent
from \pkg{stargazer}.

\pagebreak

\begin{center}
\begin{longtable}{l l l}
\toprule
Class			& Package			& Description \\
\midrule
\endfirsthead

\multicolumn{3}{r}%
{{\tablename\ \thetable{} -- continued from previous page}} \\
\toprule
Class			& Package			& Description \\
\midrule
\endhead

\midrule
\multicolumn{3}{r}{{Continued on next page}} \\
\bottomrule
\endfoot

\caption[List of 52 supported model types (version 1.30).]{\label{tab:types} List of 52 supported model types (version 1.30).} \\
\endlastfoot

`\code{aftreg}'		& \pkg{eha}			& Accelerated failure time regression \\
`\code{betareg}'	& \pkg{betareg}			& Beta regression for rates and proportions \\
`\code{brglm}'		& \pkg{brglm}			& Bias-reduced generalized linear models \\
`\code{btergm}'		& \pkg{btergm}			& Temporal exponential random graph models \\
`\code{clm}'		& \pkg{ordinal}			& Cumulative link models \\
`\code{clogit}'		& \pkg{survival}		& Conditional logistic regression \\
`\code{coeftest}'	& \pkg{lmtest}			& Wald tests of estimated coefficients \\
`\code{coxph}'		& \pkg{survival}		& Cox proportional hazard models \\
`\code{coxph.penal}'	& \pkg{survival}		& Cox proportional hazard with penalty splines \\
`\code{dynlm}'		& \pkg{dynlm}			& Time series regression \\
`\code{ergm}'		& \pkg{ergm}			& Exponential random graph models \\
`\code{gam}'		& \pkg{mgcv}			& Generalized additive models \\
`\code{gee}'		& \pkg{gee}			& Generalized estimation equation \\
`\code{glm}'		& \pkg{stats}			& Generalized linear models \\
`\code{[gl|l|nl]merMod}'& \pkg{lme4} ($<$ 1.0) 		& Generalized linear mixed models \\
`\code{gls}'		& \pkg{nlme}			& Generalized least squares \\
`\code{gmm}'		& \pkg{gmm}			& Generalized method of moments estimation \\
`\code{ivreg}'		& \pkg{AER}			& Instrumental-variable regression using 2SLS \\
`\code{hurdle}'		& \pkg{pscl}			& Hurdle regression models for count data \\
`\code{lm}'		& \pkg{stats}			& Ordinary least squares \\
`\code{lme}'		& \pkg{nlme}			& Linear mixed-effects models \\
`\code{lme4}'		& \pkg{lme4} ($\geq$ 1.0)	& Linear mixed-effects models \\
`\code{lmrob}'  	& \pkg{robustbase}		& MM-type estimators for linear models \\
`\code{lnam}'		& \pkg{sna}			& Linear network autocorrelation models \\
`\code{lrm}'		& \pkg{rms}, \pkg{Design}	& Logistic regression models \\
`\code{maBina}'		& \pkg{erer}			& Marginal effects for binary response models \\
`\code{multinom}'	& \pkg{nnet}			& Multinomial log-linear models \\
`\code{negbin}'		& \pkg{MASS}			& Negative binomial generalized linear models \\
`\code{nlme}'		& \pkg{nlme}			& Nonlinear mixed-effects models \\
`\code{phreg}'		& \pkg{eha}			& Parametric proportional hazards regression \\
`\code{plm}'		& \pkg{plm}			& Linear models for panel data \\
`\code{pmg}'		& \pkg{plm}			& Linear panel models with heterogeneous coefficients \\
`\code{polr}'		& \pkg{MASS}			& Ordered logistic or probit regression \\
`\code{Relogit}'	& \pkg{Zelig}			& Rare events logistic regression \\
`\code{rem.dyad}'	& \pkg{relevent}		& Relational event models for dyadic data \\
`\code{rlm}'		& \pkg{MASS}			& Robust fitting of linear models \\
`\code{rq}'		& \pkg{quantreg}		& Quantile regression models \\
`\code{sclm}'		& \pkg{ordinal}			& Cumulative link models \\
`\code{sienaFit}'	& \pkg{RSiena}			& Stochastic actor-oriented models for networks \\
`\code{simex}'		& \pkg{simex}			& SIMEX algorithm for measurement error models \\
`\code{stergm}'		& \pkg{tergm}			& Temporal exponential random graph models \\
`\code{survreg}'	& \pkg{survival}		& Parametric survival regression models \\
`\code{survreg.penal}'	& \pkg{survival}		& Frailty survival models \\
`\code{svyglm}' 	& \pkg{survey}			& Survey-weighted generalized linear models \\
`\code{systemfit}'	& \pkg{systemfit}		& Linear structural equations \\
`\code{texreg}'		& \pkg{texreg}			& For easy manipulation of \pkg{texreg} tables \\
`\code{tobit}'		& \pkg{AER}			& Tobit regression models for censored data \\
`\code{weibreg}'	& \pkg{eha}			& Weibull regression \\
`\code{zelig}'		& \pkg{Zelig}			& Zelig models \citep{owen2013zelig:} \\
`\code{zeroinfl}'	& \pkg{pscl}			& Zero-inflated regression models \\
\bottomrule\\
\end{longtable}
\end{center}

\vspace*{-1.2cm}

The \pkg{apsrtable} package (version 0.8-8) has custom functions for
`\code{coxph}', `\code{gee}', `\code{glm}', `\code{lm}', `\code{lrm}',
`\code{negbxin},' `\code{svyglm}' and `\code{tobit}' objects, but it
does not feature any multilevel models or network models.
The \pkg{memisc} package (version 0.95-38) features `\code{aftreg}',
`\code{betareg}', `\code{clm}', `\code{dynlm}', `\code{glm}',
`\code{hurdle}', `\code{ivreg}', `\code{lm}', `\code{lmer}',
`\code{mer}', `\code{multinom}', `\code{phreg}', `\code{polr}',
`\code{tobit}', `\code{simex}', `\code{survreg}', `\code{weibreg}',
and `\code{zeroinfl}' models but cannot handle any network models and
recent versions of \pkg{lme4} multilevel models \citep{bates2013lme4:}.

The \pkg{stargazer} package (version 3.0.1) has built-in functions for
`\code{betareg}', `\code{clm}', `\code{clogit}', `\code{coxph}',
`\code{ergm}', `\code{gam}', `\code{gee}', `\code{glm}',
`\code{glmerMod}, `\code{gls}', `\code{hurdle}', `\code{ivreg}',
`\code{lm}', `\code{lmerMod}, `\code{lmrob}', `\code{multinom}',
`\code{nlmerMod}', `\code{plm}, `\code{pmg}', `\code{polr}',
`\code{rlm}', `\code{survreg}', `\code{svyglm}, `\code{tobit}', and
`\code{zeroinfl}' objects as well as several \pkg{Zelig} adaptations
\citep{owen2013zelig:}, but it does not support custom user extensions.

\pkg{texreg} (version 1.30), in contrast, can deal with all of the
above model types (that is, the union of all three packages, except
for some \pkg{Zelig} models), is extensible, and offers additional
built-in functions for the following model classes: `\code{brglm}',
`\code{btergm}',`\code{coxph.penal}', `\code{gmm}', `\code{lme}',
`\code{lme4}', `\code{lnam}', `\code{maBina}', `\code{nlme}',
`\code{rem.dyad}', `\code{rq}', `\code{sclm}', `\code{stergm}',
`\code{systemfit}', `\code{texreg}' and `\code{zelig}' (`\code{logit}'
and `\code{relogit}') objects.  Table~\ref{tab:types} gives an
overview of currently implemented model types.

\pkg{texreg} supports Microsoft Word, HTML,
Markdown, and \pkg{knitr} whereas the other packages
(except for \pkg{xtable}) are restricted to {\LaTeX} output.
\pkg{apsrtable} has an option for \code{Sweave} integration, which
does not require any argument in \pkg{texreg}.  In the \pkg{memisc}
package and in \pkg{texreg}, the \pkg{booktabs} and \pkg{dcolumn}
{\LaTeX} packages for table layout (see
Section~\ref{tablayout}) can be used, which is not available in
\pkg{apsrtable} (and only \pkg{dcolumn} is supported in
\pkg{stargazer}).

While \pkg{apsrtable} and \pkg{texreg} allow for custom
GOF measures, \pkg{memisc} and \pkg{stargazer} only
feature a set of hardcoded statistics.  Apart from this, all packages
presented here are significantly less flexible than \pkg{texreg}
regarding the utilization of space (Section~\ref{space}), layout
options (Section~\ref{tablayout}), outlet- or model-specific
requirements (Section~\ref{specific}), and customizability
(Section~\ref{customizability}).

\section[Under the hood: How texreg works]{Under the hood: How
  \pkg{texreg} works} \label{description} The \pkg{texreg} package
consists of three main functions:

\pagebreak

\begin{enumerate}
\item \code{texreg()} for {\LaTeX} output;
\item \code{htmlreg()} for HTML,
  Markdown-compatible and Microsoft Word-compatible
  output;
\item \code{screenreg()} for text output to the \proglang{R} console.
\end{enumerate}
There are various internal helper functions, which are called from
each of these main functions for purposes of pre- and postprocessing.
Moreover, there is a class definition for `\code{texreg}' objects, and a
generic \code{extract()} function along with its methods for various
statistical models.  Figure~\ref{fig:flow} illustrates the procedure
following a call of one of the main functions.  Details about each
step are provided below.

\begin{figure}[t!]
  \begin{tikzpicture}[
      texreg/.style={rectangle, minimum height=15mm, very thick, draw=black!50, top color=white, bottom color=black!20, right},
      arrow/.style={->, line width=3, draw=black!40}
  ]
    \node (extract)	[texreg, text width=1.4cm]	at (0,0)	{extract `\code{texreg}' objects};
    \node (pre)		[texreg, text width=1.8cm]	at (2.4,0)	{preprocess `\code{texreg}' objects};
    \node (match)	[texreg, text width=2.4cm]	at (5.2,0)	{merge models and aggre\-gate matrices};
    \node (post)	[texreg, text width=1.5cm]	at (8.6,0)	{post\-pro\-cess matrices};
    \node (aggregate)	[texreg, text width=1.7cm]	at (11.15,0)	{aggregate and conflate table};
    \node (typeset)	[texreg, text width=1.2cm]	at (13.85,0)	{typeset final table};
    \draw [arrow] (extract) -- (pre);
    \draw [arrow] (pre) -- (match);
    \draw [arrow] (match) -- (post);
    \draw [arrow] (post) -- (aggregate);
    \draw [arrow] (aggregate) -- (typeset);
  \end{tikzpicture}
  \caption{Simplified flow diagram of a \code{texreg()}, \code{htmlreg()}, or \code{screenreg()} call.}
  \label{fig:flow}
\end{figure}

\subsection[The generic extract() function and its methods]{The
  generic \code{extract()} function and its methods} \label{extract}
First, the user hands over a model object or a list of models to the
\code{texreg()}, \code{htmlreg()} or \code{screenreg()} function.
This main function calls the generic \code{extract()} function in
order to retrieve all the relevant pieces of information from the
models.  The \code{extract()} function knows how to cope with various
model types (see Table~\ref{tab:types}) because it merely calls the
appropriate \code{extract()} method designed for the specific model
type.  For example, if the model is of class `\code{lm}',
\code{extract()} calls the \code{extract()} method for `\code{lm}'
objects, etc.  Custom \code{extract()} methods can be easily added (see
Section~\ref{extensions}).

An \code{extract()} method aggregates various required pieces of
information, like the coefficients, their names, standard errors, $p$
values, and several GOF measures.  Which measures are used depends on
the specific \code{extract()} method.  It is also possible to let the
user decide: beside the \code{model} argument, each extract method is
allowed to have more arguments.  For example, the \code{extract()}
method for `\code{lme4}' objects, which are \pkg{lme4} multilevel
models \citep{bates2013lme4:}, has Boolean options like
\code{include.variance}, which turns on the inclusion of random effect
variances in the GOF block, and numeric arguments like \code{conf.level},
which sets the confidence level for computation of profile or
bootstrapped confidence intervals.  When the main function
is called in the first place, the user can include these custom
arguments to finetune the behavior of the \code{extract()} methods.

Once the relevant data have been extracted from a model object, the
\code{extract()} method creates a `\code{texreg}' object by calling the
\code{createTexreg()} function and handing over the extracted data.
The `\code{texreg}' object or the list of `\code{texreg}' objects is
finally returned to the main function.

\subsection[`texreg' objects: An S4 class]{`\code{texreg}' objects: An
  \proglang{S}4 class}
There is an \proglang{S}4 class definition for `\code{texreg}' objects.  Such an
object contains four vectors for the coefficients---the coefficient
values (\code{numeric}), their names (\code{character}), standard
errors (\code{numeric}), and $p$~values (\code{numeric})---, and three
vectors for the GOF statistics: the GOF values
(\code{numeric}), their names (\code{character}), and dummy variables
indicating whether it makes sense for the GOF value to have several
decimal places (\code{logical}); for example, one would not want the
number of observations to have any decimal places.

As some types of statistical models report confidence intervals rather
than standard errors and $p$~values, the `\code{texreg}' class definition
can alternatively store lower and upper bounds of confidence intervals
instead of standard errors and $p$~values.  Which slots of the class
are used depends on the \code{extract()} method for the specific model.
The \pkg{texreg} package checks whether standard errors are present in
the `\code{texreg}' object and use either standard errors or confidence
intervals depending on availability.

The class contains validation rules which make sure that the four
coefficient vectors all have the same length and that the three GOF
vectors also all have the same length.  There are several exceptions to
this rule: the $p$~values, the confidence intervals, and the
decimal-place vector are optional and may also have a length of zero.

The `\code{texreg}' class definition was written to facilitate the
handling of the relevant pieces of information.  Handing over lists of
`\code{texreg}' objects between functions is more user-friendly than
handing over lists of nested lists of vectors.  `\code{texreg}' objects
are created by the \code{extract()} methods and handed over to the
\code{texreg} function (see Section~\ref{extract}).

\subsection[Preprocessing the `texreg' objects]{Preprocessing the `\code{texreg}' objects}
Once all `\code{texreg}' objects have been returned to the
\code{texreg()}, \code{htmlreg()} or \code{screenreg()} function, they
have to be preprocessed.  This entails two steps: first, coefficients,
standard errors or $p$~values must be replaced by user-defined
\code{numeric} vectors (for example if robust standard errors have
been manually computed).  The arguments \code{override.coef},
\code{override.se}, and \code{override.pvalues} serve to replace the
coefficients, standard errors, and $p$~values, respectively.  Second,
{\LaTeX} -specific markup codes are replaced by their
HTML or plain-text equivalents if \code{htmlreg()} or
\code{screenreg()} are called instead of \code{texreg()}.

\subsection{Matching the model terms}
After preprocessing the `\code{texreg}' objects, their contents are
arranged in three separate matrices: the \emph{coefficient block
  matrix} consists of three columns for each model (coefficient,
standard error, and $p$~value); the \emph{GOF block matrix} consists
of one column for each model and one row for each GOF
statistic and contains the GOF values; and the \emph{decimal matrix}
has the same dimensions as the GOF block matrix and indicates for each
GOF value whether it should have decimal places (e.g., R$^2$, AIC,
etc.) or whether it is an integer (e.g., number of observations,
number of groups, etc.).  All of these matrices are created by
matching the names of the coefficients or GOF names of the different
models to avoid redundancy.  The three matrices are kept separate
during the postprocessing stage and are then combined in a single
table.

\subsection{Postprocessing and rearranging of the matrices}
During the postprocessing stage, the coefficient and GOF names are
replaced by user-defined names (using the \code{custom.coef.names} and
\code{custom.gof.names} arguments), coefficient rows are removed by
applying regular expressions to the row names (using the
\code{omit.coef} argument), and coefficients/standard errors and GOF
statistics are reordered according to the user's wishes (following the
\code{reorder.coef} and \code{reorder.gof} arguments).

Renaming the coefficients or GOF names may lead to duplicate entries.
These duplicate rows must be conflated.  For example, there may be one
row with the name ``duration'' (with the \code{duration} variable only
existing in the first model) and another row with the name ``time''
(with the \code{time} variable only existing in the second model).
After renaming both rows to either of the two names, the two rows must
be conflated such that there is only one row left with the
\code{duration} coefficient in the first cell and the \code{time}
coefficient in the second cell.

Rearranging the matrix also entails checking for rows with duplicate
names which are in fact \emph{not} complementary and rearranging them
only by presenting the fullest rows first.  Furthermore, there may be
more than two duplicate rows with the same name and other complex
configurations which are handled by \pkg{texreg}.  Finally, rearranged
rows are reordered to ensure that models appear as compact as possible
in the table.

\subsection{Aggregating the table and conflating columns}
Before the data are aggregated in the final table, all coefficients,
standard errors and GOF values must be formatted according to the
specifications of the user: they have to be rounded (following the
\code{digits} argument), leading zeroes must be removed if desired by
the user (as set by the \code{leading.zero} argument), and the
\code{numeric} values are converted into \code{character} strings.

The $p$~value column of the coefficient block matrix is then used to
add significance stars or bold formatting depending on the
\code{stars}, \code{symbol}, \code{star.symbol}, and \code{bold}
arguments.  In the final table, the standard error and $p$~value
columns are removed, and the standard errors are either inserted
between the coefficient and the stars or in separate rows below the
coefficients (depending on the \code{single.row} argument).

\subsection{Typesetting the final table}
The final table is eventually translated into {\LaTeX} or
HTML code and either printed to the \proglang{R} console or
diverted to a file (depending on the \code{file} argument).  All three
functions, \code{texreg()}, \code{htmlreg()} and \code{screenreg()},
have their own custom arguments for the layout of the table.  These
specific options are listed and explained at the bottom of
Table~\ref{tab:arguments}.

\section{Examples} \label{examples}

This section gives some practical examples.  All data and model
formulae were taken from the help files of the respective models and
their packages for the sake of replicability.

\subsection[The screenreg() function]{The \code{screenreg()} function}

First, consider a simple linear model as created by the \code{lm()}
function:
%
\begin{Schunk}
\begin{Sinput}
R> ctl <- c(4.17, 5.58, 5.18, 6.11, 4.50, 4.61, 5.17, 4.53, 5.33, 5.14)
R> trt <- c(4.81, 4.17, 4.41, 3.59, 5.87, 3.83, 6.03, 4.89, 4.32, 4.69)
R> group <- gl(2, 10, 20, labels = c("Ctl", "Trt"))
R> weight <- c(ctl, trt)
R> m1 <- lm(weight ~ group)
R> m2 <- lm(weight ~ group - 1)
\end{Sinput}
\end{Schunk}
%
The coefficients, standard errors, $p$~values etc.\ of model~2 can be
displayed as follows:
%
\begin{Schunk}
\begin{Sinput}
R> summary(m2)
\end{Sinput}
\begin{Soutput}
Call:
lm(formula = weight ~ group - 1)

Residuals:
    Min      1Q  Median      3Q     Max
-1.0710 -0.4938  0.0685  0.2462  1.3690

Coefficients:
         Estimate Std. Error t value Pr(>|t|)
groupCtl   5.0320     0.2202   22.85 9.55e-15 ***
groupTrt   4.6610     0.2202   21.16 3.62e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6964 on 18 degrees of freedom
Multiple R-squared: 0.9818,	Adjusted R-squared: 0.9798
F-statistic: 485.1 on 2 and 18 DF,  p-value: < 2.2e-16
\end{Soutput}
\end{Schunk}
Next, load the \pkg{texreg} package.  The output of the two models can
be converted into a plain text table using the following command.  The
text output is shown below the \proglang{R} code.
%
\begin{Schunk}
\begin{Sinput}
R> library("texreg")
R> screenreg(list(m1, m2))
\end{Sinput}
\end{Schunk}

\begin{Schunk}
\begin{Soutput}
=================================
             Model 1    Model 2
---------------------------------
(Intercept)   5.03 ***
             (0.22)
groupTrt     -0.37       4.66 ***
             (0.31)     (0.22)
groupCtl                 5.03 ***
                        (0.22)
---------------------------------
R^2           0.07       0.98
Adj. R^2      0.02       0.98
Num. obs.    20         20
=================================
*** p < 0.001, ** p < 0.01, * p < 0.05
\end{Soutput}
\end{Schunk}
%
An arbitrary number of models can be handed over to the
\code{texreg()}, \code{htmlreg()} or\linebreak \code{screenreg()} function by
enclosing them in a \code{list}.  If only one model is converted, the
\code{list} wrapper is not needed.

\subsection[texreg(), table environments, and layout
packages]{\code{texreg()}, table environments, and layout packages}

\begin{table}[t!]
\centering
\begin{tabular}{l D{.}{.}{2.5}@{} D{.}{.}{2.5}@{} }
\toprule
            & \multicolumn{1}{c}{Model 1} & \multicolumn{1}{c}{Model 2} \\
\midrule
(Intercept) & 5.03^{***} &            \\
            & (0.22)     &            \\
groupTrt    & -0.37      & 4.66^{***} \\
            & (0.31)     & (0.22)     \\
groupCtl    &            & 5.03^{***} \\
            &            & (0.22)     \\
\midrule
R$^2$       & 0.07       & 0.98       \\
Adj. R$^2$  & 0.02       & 0.98       \\
Num. obs.   & 20         & 20         \\
\bottomrule
\multicolumn{3}{l}{\scriptsize{\textsuperscript{***}$p < 0.001$,
  \textsuperscript{**}$p < 0.01$,
  \textsuperscript{*}$p < 0.05$}}
\end{tabular}
\caption{Two linear models.}
\label{tab:3}
\end{table}


The same table can be typeset in {\LaTeX} by exchanging
\code{screenreg()} for \code{texreg()}.  In the following example, several
additional arguments are demonstrated.  The {\LaTeX} output
code is shown below the \proglang{R} code that generates the table.
The resulting table is shown in Table~\ref{tab:3}.
%
\begin{Schunk}
\begin{Sinput}
R> texreg(list(m1, m2), dcolumn = TRUE, booktabs = TRUE,
+    use.packages = FALSE, label = "tab:3", caption = "Two linear models.",
+    float.pos = "h")
\end{Sinput}

\vspace*{-0.5cm}

\begin{Soutput}
\begin{table}[h]
\begin{center}
\begin{tabular}{l D{.}{.}{2.5}@{} D{.}{.}{2.5}@{} }
\toprule
            & \multicolumn{1}{c}{Model 1} & \multicolumn{1}{c}{Model 2} \\
\midrule
(Intercept) & 5.03^{***} &            \\
            & (0.22)     &            \\
groupTrt    & -0.37      & 4.66^{***} \\
            & (0.31)     & (0.22)     \\
groupCtl    &            & 5.03^{***} \\
            &            & (0.22)     \\
\midrule
R$^2$       & 0.07       & 0.98       \\
Adj. R$^2$  & 0.02       & 0.98       \\
Num. obs.   & 20         & 20         \\
\bottomrule
\multicolumn{3}{l}{\scriptsize{\textsuperscript{***}$p < 0.001$,
  \textsuperscript{**}$p < 0.01$,
  \textsuperscript{*}$p < 0.05$}}
\end{tabular}
\caption{Two linear models.}
\label{tab:3}
\end{center}
\end{table}
\end{Soutput}
\end{Schunk}
%
The caption, label, and float position of the table are set
explicitly.  The \pkg{dcolumn} package is used to align coefficients
at their decimal separators, and the \pkg{booktabs} package is
employed to create professional horizontal rules.  These arguments can
be omitted if the two packages are not available (in this case, top,
mid and bottom rules are replaced by conventional horizontal rules,
and numeric values are horizontally aligned at the center of the
column).  The \verb+\usepackage{}+ declarations for the two packages
are suppressed because the code has to be processed by
\code{Sweave()}.

In order to omit the \verb+\begin{table}+ and \verb+\end{table}+ as
well as the \verb+\begin{center}+ and \verb+\end{center}+ code, the
\code{table} and \code{center} arguments can be used.  If \code{table
  = FALSE} and \code{center = FALSE} are set, only the \code{tabular}
environment is printed, not the \code{table} and \code{center}
environments.  In effect, the resulting table would be printed in-line
in the text.  Another reason for skipping the table environment could
be to finetune the environment manually.

Alternatively, the argument \code{sideways = TRUE} can be used to
rotate the table by 90 degrees using the \code{sidewaystable}
environment in the \pkg{rotating} package \citep{rahtz2008rotating}
instead of the default \code{table} environment.

\subsection{Custom names, omission of terms, and customization of coefficients}

Another example demonstrates how the {\LaTeX} code can be
saved in an object using the \code{return.string} argument.  The
result is shown in Table~\ref{tab:4}.
%
\begin{Schunk}
\begin{Sinput}
R> mytable <- texreg(list(m1, m2), label = "tab:4",
+    caption = "Bolded coefficients, custom notes, three digits.",
+    float.pos = "h", return.string = TRUE, bold = 0.05, stars = 0,
+    custom.note = "Coefficients with $p < 0.05$ in \\textbf{bold}.",
+    digits = 3, leading.zero = FALSE, omit.coef = "Inter")
\end{Sinput}
\end{Schunk}
%
The table can be printed to the \proglang{R} console later using the
\code{cat()} function.

\begin{table}[t!]
\centering
\begin{tabular}{l c c }
\hline
           & Model 1 & Model 2 \\
\hline
groupTrt   & $-.371$  & $\textbf{4.661}$ \\
           & $(.311)$ & $(.220)$         \\
groupCtl   &          & $\textbf{5.032}$ \\
           &          & $(.220)$         \\
\hline
R$^2$      & .073     & .982             \\
Adj. R$^2$ & .022     & .980             \\
Num. obs.  & 20       & 20               \\
\hline
\multicolumn{3}{l}{\scriptsize{Coefficients with $p < 0.05$ in \textbf{bold}.}}
\end{tabular}
\caption{Bolded coefficients, custom notes, three digits.}
\label{tab:4}
\end{table}

The example presented above introduced several additional arguments:
\code{bold = 0.05} formats all coefficients with $p$~values $< 0.05$
in bold; \code{stars = 0} means that only coefficients with $p$~values
$< 0$ are decorated with a star, which effectively suppresses all
significance stars in the table because negative $p$~values are not
possible.  Note that bold formatting cannot be used in combination
with the \code{dcolumn} argument, so decimal mark alignment is
switched off in Table~\ref{tab:4}.  The \code{booktabs} argument was
also left out to show the difference between conventional horizontal
lines in Table~\ref{tab:4} and \code{booktabs} rules in
Table~\ref{tab:3}.  The \code{custom.note = "Coefficients with $p <
  0.05$ in \textbf{bold}."} argument changes the significance note
below the table.  The \code{digits = 3} argument sets three decimal
places, \code{leading.zero = FALSE} suppresses leading zeroes before
the decimal separator, and \code{omit.coef = "Inter"} causes all rows
containing the regular expression ``Inter'' to be skipped from the
output (here: the ``(Intercept)'' term).  Note that more complex
regular expressions are possible; for example, \code{omit.coef =
  "(Trt)|(Ctl)"} would remove all rows matching either ``Trt'' or
``Ctl''.

\subsection[Multiple model types, single.row, and custom
names]{Multiple model types, \code{single.row}, and custom names}

Another example shows how \pkg{texreg} can deal with multiple
\emph{kinds} of models in the same table.  The following code shows
how ordinary least squares (OLS) and generalized least squares (GLS)
models are matched in a single output table.  The output is shown in
Table~\ref{tab:5}.
%
\begin{Schunk}
\begin{Sinput}
R> library("nlme")
R> m3 <- gls(follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time), Ovary,
+    correlation = corAR1(form = ~ 1 | Mare))
R> table <- texreg(list(m1, m3), custom.coef.names = c(
+      "Intercept",
+      "Control",
+      "$\\sin(2 \\cdot \\pi \\cdot \\mbox{time})$",
+      "$\\cos(2 \\cdot \\pi \\cdot \\mbox{time})$"),
+    custom.model.names = c("OLS model", "GLS model"),
+    reorder.coef = c(1, 3, 4, 2),
+    caption = "Multiple model types, custom names, and single row.",
+    label = "tab:5", stars = c(0.01, 0.001), dcolumn = TRUE,
+    booktabs = TRUE, use.packages = FALSE, single.row = TRUE,
+    include.adjrs = FALSE, include.bic = FALSE)
\end{Sinput}
\end{Schunk}
%
\begin{table}[t!]
\centering
\begin{tabular}{l D{)}{)}{11)2}@{} D{)}{)}{11)2}@{} }
\toprule
                                      & \multicolumn{1}{c}{OLS model} & \multicolumn{1}{c}{GLS model} \\
\midrule
Intercept                             & 5.03 \; (0.22)^{**} & 12.22 \; (0.66)^{**} \\
$\sin(2 \cdot \pi \cdot \mbox{time})$ &                     & -2.77 \; (0.65)^{**} \\
$\cos(2 \cdot \pi \cdot \mbox{time})$ &                     & -0.90 \; (0.70)      \\
Control                               & -0.37 \; (0.31)     &                      \\
\midrule
R$^2$                                 & 0.07                &                      \\
Num. obs.                             & 20                  & 308                  \\
AIC                                   &                     & 1571.45              \\
Log Likelihood                        &                     & -780.73              \\
\bottomrule
\multicolumn{3}{l}{\scriptsize{\textsuperscript{**}$p < 0.001$,
  \textsuperscript{*}$p < 0.01$}}
\end{tabular}
\caption{Multiple model types, custom names, and single row.}
\label{tab:5}
\end{table}
%
Several interesting things can be noted.  First, the
\code{custom.coef.names} argument was used to relabel the coefficient
rows.  If there were repetitions of coefficient names in the\linebreak
\code{custom.coef.names} vector, \pkg{texreg} would try to conflate
rows with identical names.  In the case shown here, the two models are
only matched on the intercept and the number of observations because
all other rows have unique names.

Second, the custom names include {\LaTeX} code.  Within the
code, in-line math code is allowed.  {\LaTeX} commands have
to be marked by an additional backslash as an escape character, e.g.,
\verb+\\pi+ instead of \verb+\pi+.  Text within math blocks can be
included in \verb+\mbox{}+ commands.

Third, custom names were also provided for the models.  Using the
\code{custom.model.names} argument, the default ``Model 1'', ``Model
2'' etc.\ are replaced by ``OLS model'' and ``GLS model'' in this
case.

Fourth, the order of the coefficients was changed using the
\code{reorder.coef} argument.  The ``Control'' term was moved to the
last position in the table.

Fifth, two significance levels (and, accordingly, a maximum of two
stars) are used in the table.  The \code{stars} argument takes at most
four values, and when four values are specified, the lowest
significance level (usually $0.05 \leq p < 0.1$) is denoted by the
character specified in the \code{symbol} argument (by default a
centered dot).

Sixth, the \code{single.row} argument causes the table to consume less
vertical and more horizontal space because the standard errors are
inserted right after the coefficients.

And seventh, the \code{include.adjrs} and \code{include.bic} arguments
suppress the inclusion of the adjusted R$^2$ and BIC GOF
statistics.  These are model-specific arguments, which are defined in
the \code{extract()} methods for `\code{lm}' and `\code{gls}'.  More
information about model-specific arguments can be found on the help
page of the generic \code{extract()} function.

\subsection{An example with robust standard errors}

A common task in econometrics is to report robust---i.e.,
(Eicker-)Huber-White-corrected, or heteroskedasticity-consistent---standard
errors using the \pkg{sandwich}
\citep{zeileis2004econometric,zeileis2006object} and \pkg{lmtest}
\citep{zeileis2002diagnostic} packages.  The following code shows how
this can be accomplished in combination with the \pkg{texreg} package.
The resulting table is not reported here.
%
\begin{Schunk}
\begin{Sinput}
R> library("sandwich")
R> library("lmtest")
R> hc <- vcovHC(m2)
R> ct <- coeftest(m2, vcov = hc)
R> se <- ct[, 2]
R> pval <- ct[, 4]
R> texreg(m2, override.se = se, override.pvalues = pval)
\end{Sinput}
\end{Schunk}
%
The standard errors and $p$~values are first extracted from the
\code{hc} matrix and then handed over to the \code{texreg()} function
using the \code{override.se} and \code{override.pvalues} arguments.

\subsection[htmlreg(), Microsoft Word, knitr, and Markdown]{\code{htmlreg()},
  Microsoft Word, \pkg{knitr}, and Markdown}

The following examples show how the \code{htmlreg()} function can be
used.  The output code for these examples is not reported here.

The output of any \code{texreg()}, \code{htmlreg()} or
\code{screenreg()} call can be written directly to a file by adding
the \code{file} argument.  This is especially handy because
HTML files can be read by Microsoft Word if a
``.doc'' file extension is added.

If the table is exported to a file, it is advisable to include the
full header information of the HTML file to make sure that
Microsoft Word or other programs can parse the file.  An example:
%
\begin{Schunk}
\begin{Sinput}
R> htmlreg(list(m1, m2, m3), file = "mytable.doc", inline.css = FALSE,
+    doctype = TRUE, html.tag = TRUE, head.tag = TRUE, body.tag = TRUE)
\end{Sinput}
\end{Schunk}
%
The \code{doctype} argument adds the document type declaration to the
first line of the HTML document.  The \code{inline.css =
  FALSE} argument causes the function to write cascading style sheets
(the table formatting code) into the \code{<head>} tag rather than
into the table code.  The \code{head.tag} argument actually adds such
a \code{<head>} tag to the document.  Similarly, the \code{body.tag}
argument wraps the table in a \code{<body>} tag, and the
\code{html.tag} argument encloses both---the \code{<head>} and the
\code{<body>} tag---in an \code{<html>} tag.  In other words, these
arguments create a whole HTML document rather than merely
the table code.  The resulting file can be read by Microsoft Word
because the HTML file has a ``.doc'' extension.

The \texttt{htmlreg()} function also works well with the \pkg{knitr}
package for dynamic report generation \citep{xie2012knitr}.  The
default arguments are compatible with \pkg{knitr} and
HTML.  In addition to HTML, \pkg{knitr} is also
compatible with Markdown, a simplified markup language.
\pkg{texreg} can work with Markdown as well, but an
additional argument should be provided to make it work:
%
\begin{Schunk}
\begin{Sinput}
R> htmlreg(list(m1, m2, m3), star.symbol = "\\*", center = TRUE)
\end{Sinput}
\end{Schunk}
%
The \verb+star.symbol = "\\*"+ argument makes sure that
Markdown does not interpret the significance stars as
special Markdown syntax.  The additional (and optional)
\code{center = TRUE} argument centers the table horizontally on the
page.

\subsection{Confidence intervals instead of standard errors}

\begin{table}[t!]
\centering
\begin{tabular}{l c c c }
\toprule
            & Model 1 & Model 2 & Model 3 \\
\midrule
(Intercept) & $\textbf{5.03} \; (0.22)^{***}$ & $\textbf{5.03} \; [4.60;\ 5.46]^{*}$ &                                      \\
groupTrt    & $-0.37 \; (0.31)$               & $-0.37 \; [-0.98;\ 0.24]$            & $\textbf{4.66} \; [4.23;\ 5.09]^{*}$ \\
groupCtl    &                                 &                                      & $\textbf{5.03} \; [4.60;\ 5.46]^{*}$ \\
\bottomrule
\multicolumn{4}{l}{\scriptsize{\textsuperscript{***}$p<0.001$,
  \textsuperscript{**}$p<0.01$,
  \textsuperscript{*}$p<0.05$ (or 0 outside the confidence interval).}}
\end{tabular}
\caption{Enforcing confidence intervals.}
\label{table:coefficients}
\end{table}

Most model types implemented in \pkg{texreg} report standard errors
and $p$~values.  However, some model types report confidence intervals
by default.  The \pkg{btergm} package \citep{leifeld2013btergm:} and
the \pkg{lme4} package \citep{bates2013lme4:} are two examples.  If
confidence intervals are preferred to standard errors but they are not
available by default, the \code{ci.force} argument allows conversion
of standard errors to confidence intervals.  The \code{ci.force.level}
argument determines at which confidence level the interval should be
computed.

A star is added to estimates where the confidence interval does not
contain the value given by \code{ci.test} (to remove significance stars,
\code{ci.test = NULL} can be set).  When the \code{bold} argument is
used in conjunction with confidence intervals, \code{bold} values
greater than $0$ cause \pkg{texreg} to print estimates in bold where
the \code{ci.test} value is outside the confidence interval, regardless
of the actual value of the \code{bold} argument (see Table~\ref{table:coefficients}):
%
\begin{Schunk}
\begin{Sinput}
R> texreg(list(m1, m1, m2), ci.force = c(FALSE, TRUE, TRUE), ci.test = 0,
+    ci.force.level = 0.95, bold = 0.05, float.pos = "h",
+    caption = "Enforcing confidence intervals.",
+    booktabs = TRUE, use.packages = FALSE, single.row = TRUE)
\end{Sinput}
\end{Schunk}

\section{Writing extensions for new models} \label{extensions}
The previous examples have demonstrated how the \pkg{texreg} package
can be used to convert statistical model output into plain-text,
{\LaTeX}, HTML, and Markdown tables.
Yet, this only works with model types known to \pkg{texreg}.
Accordingly, this section shows how methods for new model types can be
devised and registered.

\begin{table}[b!]
\centering
  \begin{tabular}{l p{12.3cm}}
   \toprule
   Arguments		& Description \\
   \midrule
   \code{coef.names}	& The names of the independent variables or coefficients. \\
   \code{coef}		& The actual coefficients. These values must be in the same order as the \code{coef.names}. \\
   \code{se}		& (\emph{optional}) The standard errors, which will later be put in parentheses. These values must be in the same order as the \code{coef.names}. \\
   \code{pvalues}	& (\emph{optional}) The $p$~values. They are used to add significance stars. These values must be in the same order as the \code{coef.names}. \\
   \code{ci.low}	& (\emph{optional}) Lower bounds of the confidence intervals.  An alternative to the \code{se} slot. \\
   \code{ci.up}		& (\emph{optional}) Upper bounds of the confidence intervals.  An alternative to the \code{se} slot. \\
   \code{gof.names}	& The names of some GOF statistics to be added to the table. For example, the \code{extract()} method for `\code{lm}' objects extracts R$^2$, Adj.\ R$^2$ and Num.\ obs. \\
   \code{gof}		& A vector of GOF statistics to be added to the table. These values must be in the same order as the \code{gof.names}. \\
   \code{gof.decimal}	& (\emph{optional}) A vector of logical (Boolean) values indicating for every GOF value whether the value should have decimal places in the output table. This is useful to avoid decimal places for the number of observations and similar count variables. \\
   \bottomrule
  \end{tabular}
 \caption{Arguments of the \code{createTexreg()} function.}
 \label{tab:vectors}
\end{table}

\subsection{Simple extensions}
A custom extract function can be easily implemented.  For any model
type, there exists a function which extracts the relevant information
from a model.  For example, the \code{extract()} method for `\code{lm}'
objects provides coefficients and GOF statistics for
`\code{lm}' objects, the \code{extract()} method for `\code{ergm}' objects
provides this information for `\code{ergm}' objects, etc.

To get an overview of the model type one is interested in, it is
recommended to fit a model and examine the resulting object using the
\code{str(model)} command, the \code{summary(model)} command, the
\code{summary(model)\$coef} command, and related approaches.

Any new extract function should retrieve the data shown in Table~\ref{tab:vectors} from a statistical model.
Note that \code{pvalues} and \code{gof.decimal} are optional and can
be omitted.  Either the \code{se} slot or the \code{ci.low} and
\code{ci.up} slots must contain values.

Once these data have been located and extracted, a `\code{texreg}'
object can be created and returned to the \code{texreg()} function.
The following code provides a simple example for `\code{lm}' objects:
%
\begin{Schunk}
\begin{Sinput}
R> extract.lm <- function(model) {
+    s <- summary(model)
+    names <- rownames(s$coef)
+    co <- s$coef[, 1]
+    se <- s$coef[, 2]
+    pval <- s$coef[, 4]
+
+    rs <- s$r.squared
+    adj <- s$adj.r.squared
+    n <- nobs(model)
+
+    gof <- c(rs, adj, n)
+    gof.names <- c("R$^2$", "Adj.\\ R$^2$", "Num.\\ obs.")
+
+    tr <- createTexreg(
+        coef.names = names,
+        coef = co,
+        se = se,
+        pvalues = pval,
+        gof.names = gof.names,
+        gof = gof
+    )
+    return(tr)
+  }
\end{Sinput}
\end{Schunk}
%
First, the names of the model terms, the coefficient values, the
standard errors, and the $p$~values are extracted from the model or
its summary (they can be computed if not available).

Second, various summary statistics and GOF measures are
extracted from the model object (in this case: R$^2$, Adj.\ R$^2$ and
Num.\ obs.) and saved in a \code{numeric} vector.

Third, the names of these statistics should be defined in a
\code{character} vector.  All vectors so far should have the same
length.

Fourth, a new `\code{texreg}' object should be created, with the
information extracted before included as arguments.

Fifth, the `\code{texreg}' object must be returned.  This is necessary
for the \code{texreg()} function to continue processing the model.

After writing a custom function, the function has to be registered as
a method for the generic \code{extract()} function.  In the above
example, this can be achieved with the following code:
%
\begin{Schunk}
\begin{Sinput}
R> setMethod("extract", signature = className("lm", "stats"),
+    definition = extract.lm)
\end{Sinput}
\end{Schunk}
%
Assume, for instance, that an extension for `\code{clogit}' objects
called \code{extract.clogit()} is written.  The \code{clogit()}
function (and the corresponding class definition) can be found in the
\pkg{survival} package \citep{survival-book, therneau2012package}.  Then the code
above should be changed as follows:
%
\begin{Schunk}
\begin{Sinput}
R> setMethod("extract", signature = className("clogit", "survival"),
+    definition = extract.clogit)
\end{Sinput}
\end{Schunk}
%
After executing the definition of the function and the adjusted
\code{setMethod()} command, \pkg{texreg} can be used with
`\code{clogit}' models.

\subsection{A complete example}
The following code shows the complete \code{extract.lm()} function as
included in the \pkg{texreg} package.
%
\begin{Schunk}
\begin{Sinput}
R> extract.lm <- function(model, include.rsquared = TRUE,
+      include.adjrs = TRUE, include.nobs = TRUE, ...) {
+
+    s <- summary(model, ...)
+    names <- rownames(s$coef)
+    co <- s$coef[, 1]
+    se <- s$coef[, 2]
+    pval <- s$coef[, 4]
+
+    gof <- numeric()
+    gof.names <- character()
+    gof.decimal <- logical()
+    if (include.rsquared == TRUE) {
+      rs <- s$r.squared
+      gof <- c(gof, rs)
+      gof.names <- c(gof.names, "R$^2$")
+      gof.decimal <- c(gof.decimal, TRUE)
+    }
+    if (include.adjrs == TRUE) {
+      adj <- s$adj.r.squared
+      gof <- c(gof, adj)
+      gof.names <- c(gof.names, "Adj.\\ R$^2$")
+      gof.decimal <- c(gof.decimal, TRUE)
+    }
+    if (include.nobs == TRUE) {
+      n <- nobs(model)
+      gof <- c(gof, n)
+      gof.names <- c(gof.names, "Num.\\ obs.")
+      gof.decimal <- c(gof.decimal, FALSE)
+    }
+
+    tr <- createTexreg(
+        coef.names = names,
+        coef = co,
+        se = se,
+        pvalues = pval,
+        gof.names = gof.names,
+        gof = gof,
+        gof.decimal = gof.decimal
+    )
+    return(tr)
+  }
R> setMethod("extract", signature = className("lm", "stats"),
+    definition = extract.lm)
\end{Sinput}
\end{Schunk}
%
In addition to the simple example code shown above, this function has
several arguments, which can be used to include or exclude various GOF
or summary statistics.  Additional arguments can also be used in other
contexts.  For example, the user can decide whether random effect
variances should be included in \pkg{texreg} tables of `\code{mer}'
objects \citep[from the \pkg{lme4} package, see][]{bates2013lme4:} by
setting the \code{include.variance} argument.  Similarly, the output
of `\code{stergm}' models \citep{Hunter:2008, handcock2012fit} or
`\code{hurdle}' or `\code{zeroinfl}' models
\citep{zeileis2008regression} can be typeset in two columns using the
\code{beside} argument.

New extract functions and methods can be easily used locally.  Once
they work well, submission of new extract functions to the online
forum of \pkg{texreg} is encouraged.

Existing functions can also be manipulated and overwritten locally in
order to change the GOF statistics block.

% \section{Installation and help} \label{help}

% It should be possible to install \pkg{texreg} using a simple command:
% %
% \begin{Schunk}
% \begin{Sinput}
% R> install.packages("texreg")
% \end{Sinput}
% \end{Schunk}
% %
% \pkg{texreg} is hosted on the R-Forge repository, which means that the
% most recent version can be installed with this command (often more
% recent than the CRAN version in the previous command):
% %
% \begin{Schunk}
% \begin{Sinput}
% R> install.packages("texreg", repos = "https://R-Forge.R-project.org")
% \end{Sinput}
% \end{Schunk}
% %
% The package can be updated to the most recent version by typing:
% %
% \begin{Schunk}
% \begin{Sinput}
% R> update.packages("texreg", repos = "https://R-Forge.R-project.org")
% \end{Sinput}
% \end{Schunk}
% %
% Alternatively, the source files and binaries can be downloaded from
% the \pkg{texreg} homepage
% (\url{https://r-forge.r-project.org/projects/texreg/}) and installed
% manually by entering something like
% \begin{Code}
% $ R CMD INSTALL texreg_1.xx.tar.gz
% \end{Code}
% %
% (replace \code{xx} by the current version number) on the terminal (not the \proglang{R} terminal, but the command line of the operating system).

% After loading the package, its help page can be displayed as follows:
% %
% \begin{Schunk}
% \begin{Sinput}
% R> help(package = "texreg")
% \end{Sinput}
% \end{Schunk}
% %
% More specific help on the \code{texreg()} command can be obtained by
% entering the following command once the package has been loaded:
% %
% \begin{Schunk}
% \begin{Sinput}
% R> help("texreg")
% \end{Sinput}
% \end{Schunk}
% %
% To get an overview of currently implemented extract functions and
% methods, one of these two commands can be used.
% %
% \begin{Schunk}
% \begin{Sinput}
% R> help("extract")
% R> help("extract-methods")
% \end{Sinput}
% \end{Schunk}
% %
% If all else fails, more help can be obtained from the homepage of the
% \pkg{texreg} package.  Questions can be posted to a public forum at
% \url{https://r-forge.r-project.org/projects/texreg/}.

\section*{Acknowledgments}
The author would like to thank Oleg Badunenko, Tom Carsey, S.\,Q. Chang,
Skyler Cranmer, Sebastian Daza, Christopher Gandrud, Lena Koerber,
Johannes Kutsam, Fabrice Le Lec, Florian Oswald, Markus Riester,
Francesco Sarracino, Matthieu Stigler, Sebastian Ugbaje, G{\'a}bor
Uhrin, Antoine Vernet, Yanghao Wang, and Yihui Xie for their valuable
input and ideas.

\bibliography{texreg}

\end{document}
