<commit_before>%
%  PyMC User's Guide for submission to the Journal of Statistical Software
%
%  Created by <NAME> on 2008-11-12.
%  Copyright (c) 2008 . All rights reserved.
%
\documentclass[]{jss}

\usepackage{hyperref}
\usepackage{boxedminipage}

\author{<NAME>\\University of Oxford \And 
        <NAME>\\Institut National de la\\Recherche Scientifique \And
		<NAME>\\University of Otago}
\title{\pkg{PyMC} 2.0: Bayesian Stochastic Modelling in \proglang{Python}}

%% for pretty printing and a nice hypersummary also set:
\Plainauthor{<NAME>, <NAME>, <NAME>} %% comma-separated
\Plaintitle{PyMC 2.0: Bayesian Stochastic Modelling in Python} %% without formatting
\Shorttitle{PyMC 2.0} %% a short title (if necessary)

\Abstract{
  The abstract of the article.
}
\Keywords{statistics, Markov chain Monte Carlo, simulation, \proglang{Python}}
\Plainkeywords{statistics, Markov chain Monte Carlo, simulation, Python}

%% publication information
%% NOTE: Typically, this can be left commented and will be filled out by the technical editor
%% \Volume{13}
%% \Issue{9}
%% \Month{September}
%% \Year{2004}
%% \Submitdate{2004-09-29}
%% \Acceptdate{2004-09-29}

\begin{document}

%\maketitle

%\tableofcontents
%\linenumbers
\section[Introduction]{Introduction} 
\label{sec:intro} 
\input{README}

\section[Installation]{Installation} 
\label{sec:install} 
\input{INSTALL}

\section[Tutorial]{Tutorial}
\label{sec:tutorial}
\input{tutorial}

\section[Building Models]{Building models}
\label{sec:modelbuilding} 
\input{modelbuilding}

\section[Fitting Models]{Fitting models}
\label{sec:modelfitting}
\input{modelfitting}

\section[Sampling Results]{Saving and managing sampling results}
\label{sec:database} 
\input{database}

\section[Model Checking]{Model checking and diagnostics} 
\label{sec:modelchecking}
\input{modelchecking}

\section[Extending PyMC]{Extending PyMC}
\label{sec:extending}
\input{extending}

\appendix[MCMC]{Markov chain Monte Carlo} 
\label{sec:MCMC} 
\input{theory}

\appendix[Distributions]{Probability distributions} 
\label{sec:distributions}
Several probability distributions are provided in the module \module{distributions}. A probability distribution consists of:
\begin{itemize}
    \item A function that evaluates its log-probability or log-density: \code{normal_like()}.
    \item A function that draws random variables: \code{rnormal()}.
    \item A function that computes the expectation associated with the distribution: \code{normal_expval()}.
    \item A \code{Stochastic} subclass generated from the distribution: \code{Normal}.
\end{itemize} 
\input{distributions-module}

\bibliographystyle{jss}
\bibliography{pymc}

\end{document}
 <commit_msg>added commands to jss_article to support the files created by rst2latex. added comments on latex compatibility problems between python.sty and jss.sty.<commit_after>%
%  PyMC User's Guide for submission to the Journal of Statistical Software
%
%  Created by <NAME> on 2008-11-12.
%  Copyright (c) 2008 . All rights reserved.
%
\documentclass[]{jss}


%\usepackage{python}
\usepackage{epsfig}
\usepackage{hyperref}
\usepackage{boxedminipage}

\author{<NAME>\\University of Oxford \And 
        <NAME>\\McGill University  \And
		<NAME>\\University of Otago}
\title{\pkg{PyMC} 2.0: Bayesian Stochastic Modelling in \proglang{Python}}

%% for pretty printing and a nice hypersummary also set:
\Plainauthor{<NAME>, <NAME>, <NAME>} %% comma-separated
\Plaintitle{PyMC 2.0: Bayesian Stochastic Modelling in Python} %% without formatting
\Shorttitle{PyMC 2.0} %% a short title (if necessary)

\Abstract{
  The abstract of the article.
}
\Keywords{statistics, Markov chain Monte Carlo, simulation, \proglang{Python}}
\Plainkeywords{statistics, Markov chain Monte Carlo, simulation, Python}

%% publication information
%% NOTE: Typically, this can be left commented and will be filled out by the technical editor
%% \Volume{13}
%% \Issue{9}
%% \Month{September}
%% \Year{2004}
%% \Submitdate{2004-09-29}
%% \Acceptdate{2004-09-29}

%%%%%%%%%%%%%%% Commands from rst2latex %%%%%%%%%%%%%%%%%%%%%%%%
% These are used in the tex files generated by rst2latex, namely
% README, INSTALL and database
\newcommand{\rubric}[1]{\subsection*{~\hfill {\it #1} \hfill ~}}
\newcommand{\titlereference}[1]{\textsl{#1}}
\newlength{\locallinewidth}
\setlength{\locallinewidth}{7in}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% The tex files use constructs from the python.sty style file, 
% such as \module and \code. The problem is that many commands
% defined in python.sty are also defined in jss.sty, creating
% clashes. In other words, jss is not compatible with python. 
% We can either tweak python.sty or jss.sty. We simply need to 
% either remove the offending definitions or use renewcommand
% instead of newcommand. Let me know what you think. 


\begin{document}

%\maketitle

%\tableofcontents
%\linenumbers
\section[Introduction]{Introduction} 
\label{sec:intro} 
\input{README}

\section[Installation]{Installation} 
\label{sec:install} 
\input{INSTALL}

\section[Tutorial]{Tutorial}
\label{sec:tutorial}
\input{tutorial}

\section[Building Models]{Building models}
\label{sec:modelbuilding} 
\input{modelbuilding}

\section[Fitting Models]{Fitting models}
\label{sec:modelfitting}
\input{modelfitting}

\section[Sampling Results]{Saving and managing sampling results}
\label{sec:database} 
\input{database}

\section[Model Checking]{Model checking and diagnostics} 
\label{sec:modelchecking}
\input{modelchecking}

\section[Extending PyMC]{Extending PyMC}
\label{sec:extending}
\input{extending}

\appendix[MCMC]{Markov chain Monte Carlo} 
\label{sec:MCMC} 
\input{theory}

\appendix[Distributions]{Probability distributions} 
\label{sec:distributions}
Several probability distributions are provided in the module \module{distributions}. A probability distribution consists of:
\begin{itemize}
    \item A function that evaluates its log-probability or log-density: \code{normal_like()}.
    \item A function that draws random variables: \code{rnormal()}.
    \item A function that computes the expectation associated with the distribution: \code{normal_expval()}.
    \item A \code{Stochastic} subclass generated from the distribution: \code{Normal}.
\end{itemize} 
%\input{distributions-module}

\bibliographystyle{jss}
\bibliography{pymc}

\end{document}
 
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