From 505dc48de7ad92d16f7ce2452dd997bc7b519e8e Mon Sep 17 00:00:00 2001 From: AJ Rossini Date: Wed, 11 Mar 2009 17:54:08 +0100 Subject: [PATCH] clean up, insert first version of Lisp stuff. Signed-off-by: AJ Rossini --- Doc/Rossini-RiceU-2009Mar.tex | 728 ++++++++++++++++++++++++------------------ 1 file changed, 417 insertions(+), 311 deletions(-) diff --git a/Doc/Rossini-RiceU-2009Mar.tex b/Doc/Rossini-RiceU-2009Mar.tex index 5df72de..6ee64b5 100644 --- a/Doc/Rossini-RiceU-2009Mar.tex +++ b/Doc/Rossini-RiceU-2009Mar.tex @@ -55,16 +55,18 @@ \section{Preliminaries} -\begin{frame}{Goals}{(define, strategic approach, justify)} +\begin{frame}{Goals for this Talk}{(define, strategic approach, + justify)} \begin{itemize} \item To describe the concept of \alert{computable and executable statistics}. \item To demonstrate that \alert{there exists a research program} - consisting of simple steps which is adaptable to a practitioner's - work habits, which is feasible and introduces relatively minimal - disruptive changes. + consisting of simple steps which can increase the use of + statistical computing approaches to allow for clear description + not only of the numerical characteristics of procedures, but the + statistical concepts behind them. \item To justify that the \alert{approach is worthwhile} and represents a staged effort towards \alert{increased use of best @@ -73,6 +75,33 @@ (unfortunately, the last is still incomplete) \end{frame} +\begin{frame}{Philosophically, why Common Lisp?} + Philosophically: + \begin{itemize} + \item Lisp as an ancient ``AI'' language; Statistics as ``artificial + intelligence'' (not real intelligence, \alert{humans are too + flawed and inconsistent} for Bayesian work to be anything but + AI). + \item Semantics and context are important: well supported by Lisp + paradigms. + \item Lisp's parentheses describe single, multi-scale, + \alert{complete thought}. See \#1 for why that could make it + difficult. + \end{itemize} + +\end{frame} + +\begin{frame}{Technically, why Common Lisp?} + \begin{itemize} + \item interactive COMPILED language (``R with a compiler'') + \item CLOS is R's S4 object system ``done right''. + \item clean semantics + \item programs are data, data are programs, leading to + \item Most modern computing tools available (XML, WWW technologies) + \item ``executable XML'' + \end{itemize} +\end{frame} + \subsection{Background} \begin{frame}{Many systems existed concurrently for statistical @@ -92,21 +121,6 @@ \end{itemize} \end{frame} -\begin{frame}{Why Lisp?} - \begin{itemize} - \item Lisp as an ancient ``AI'' language; Statistics as ``artificial - intelligence'' (not real intelligence, \alert{humans are too - flawed and inconsistent} for Bayesian work to be anything but - AI). - \item Semantics and context are important: well supported by Lisp - paradigms. - \item Lisp's parentheses describe single, multi-scale, - \alert{complete thought}. See \#1 for why that could make it - difficult. - \end{itemize} - Aside: Common Lisp is the building block for all my current research - projects. -\end{frame} \begin{frame} \frametitle{Semantics and Statistics} @@ -149,12 +163,12 @@ \end{itemize} \end{frame} -\subsection{Illustrating Computable / Executable Statistics} +\section{Computable and Executable Statistics} -\begin{frame}{Can we compute?} +\begin{frame}{Can we compute with them?} - For the following examples, the critical question becomes: - \centerline{\alert{Can we compute with it?}} + For the following examples, consider whether one can ``compute'' + with the information given. \end{frame} @@ -163,13 +177,13 @@ Let $f(x;\theta)$ describe the likelihood of XX under the following assumptions. \begin{enumerate} - \item assumption 1 - \item assumption 2 + \item assumption-1 + \item assumption-2 \end{enumerate} Then if we use the following algorithm: \begin{enumerate} - \item step 1 - \item step 2 + \item step-1 + \item step-2 \end{enumerate} then $\hat{\theta}$ should be $N(0,\hat\sigma^2)$ with the following characteristics\ldots @@ -185,14 +199,65 @@ \end{itemize} \end{frame} +\begin{frame}[fragile]{Realizing Theory} + \small{ +\begin{verbatim} +(define-theorem my-proposed-theorem + (:theorem-type '(distribution-properties + frequentist + likelihood)) + (:assumes '(assumption-1 assumption-2)) + (:likelihood-form + (defun likelihood (data theta gamma) + (exponential-family theta gamma))) + (:compute-by + '(progn + (compute-starting-values thetahat gammahat + (until (convergence) + (setf convergence + (or (step-1 thetahat) + (step-2 gammahat))))))) + (:claim (assert + (and (equal-distribution thetahat normal) + (equal-distribution gammahat normal))))) +\end{verbatim} + } +\end{frame} + +\begin{frame}[fragile]{It would be nice to have} +\begin{verbatim} + (theorem-veracity 'my-proposed-theorem) +\end{verbatim} +\end{frame} + +\begin{frame}[fragile]{and why not...?} +\begin{verbatim} + (when (theorem-veracity + 'my-proposed-theorem) + (write-paper 'my-proposed-theorem + :style :JASA + :output-format + '(LaTeX MSWord))) +\end{verbatim} +\end{frame} + +\begin{frame}{Comments} + \begin{itemize} + \item The general problem is very difficult + \item Some progress has been made in small areas of basic statistics + \item Areas targetted for medium-term future: resampling methods, + likelihood theory and algorithms. + \end{itemize} + +\end{frame} \begin{frame} \frametitle{Example 2: Practice\ldots} \label{example2} The dataset comes from a series of clinical trials. We model the - primary endpoint, ``relief'', as a binary random variable. There is a random - trial effect on relief as well as severity due to differences in - recruitment and inclusion/exclusion criteria. + primary endpoint, ``relief'', as a binary random variable. There is + a random trial effect on relief as well as severity due to + differences in recruitment and inclusion/exclusion criteria. \end{frame} \begin{frame} @@ -205,6 +270,23 @@ \end{itemize} \end{frame} +\begin{frame}[fragile]{Can we compute?} +\begin{verbatim} + (dataset paper-1 + :context 'clinical-trials + :variables '((relief :model-type dependent + :distribution binary) + (trial :model-type independent + :distribution categorical) + (disease-severity)) + :metadata '(inclusion-criteria + exclusion-criteria + recruitment-rate)) + (propose-analysis paper-1) ; => '(tables + ; (logistic regression)) +\end{verbatim} +\end{frame} + \begin{frame}{Example 3: The Round-trip\ldots} \label{example3} The first examples describe ``ideas $\rightarrow$ code'' @@ -224,54 +306,14 @@ \textit{(Q: have you actually read an R package that wasn't yours?)} \end{frame} -\section{Motivation} - -\subsection{IT Can Speed up Deliverables in Statistical Practice} +\begin{frame}{Exercise left to the reader!} -\begin{frame}{Our Generic Work-flow and Life-cycle} - {describing most data analytic activities} - Workflow: - \begin{enumerate} - \item Scope out the problem - \item Sketch out a potential solution - \item Implement until road-blocks appear - \item Deliver results - \end{enumerate} - - Lifecycle: - \begin{enumerate} - \item paper sketch - \item 1st e-draft of text/code/date (iterate to \#1, discarding) - \item cycle through work - \item publish - \item ``throw-away'' - \end{enumerate} - but there is valuble information that could enable the next - generation! + (aside: I have been looking at the \textbf{stats} and \textbf{lme4} + packages recently -- \textit{for me}, very clear numerically, much + less so statistically) \end{frame} -\begin{frame}[fragile]{Paper $\rightarrow$ Computer $\rightarrow$ Article $\rightarrow$ Computer}{Cut and Paste makes for large errors.} - \begin{itemize} - \item Problems in a regulatory setting - \item Regulatory issues are just ``best practices'' - \end{itemize} - Why do we ``copy/paste'', or analogously, restart our work? - - pro: - \begin{itemize} - \item every time we repeat, we reinforce the idea in our brain - \item review of ideas can help improve them - \end{itemize} - con: - \begin{itemize} - \item inefficiency - \item introduction of mistakes - \item loss of historical context - \item changes to earlier work (on a different development branch) - can not propagate. - \end{itemize} -\end{frame} \subsection{Literate Programming is insufficient} @@ -428,8 +470,58 @@ % \end{enumerate} % \end{frame} + +\subsection{Common Lisp Statistics} + +\begin{frame} + \frametitle{Interactive Programming} + \framesubtitle{Everything goes back to being Lisp-like} + \begin{itemize} + \item Interactive programming (as originating with Lisp): works + extremely well for data analysis (Lisp being the original + ``programming with data'' language). + \item Theories/methods for how to do this are reflected in styles + for using R. + \end{itemize} +\end{frame} + +\begin{frame}[fragile] + \frametitle{Lisp} + + Lisp (LISt Processor) is different than most high-level computing + languages, and is very old (1956). Lisp is built on lists of things + which are evaluatable. +\begin{verbatim} +(functionName data1 data2 data3) +\end{verbatim} + or ``quoted'': +\begin{verbatim} +'(functionName data1 data2 data3) +\end{verbatim} + which is shorthand for +\begin{verbatim} +(list functionName data1 data2 data3) +\end{verbatim} + The difference is important -- lists of data (the second/third) are + not (yet?!) functions applied to (unencapsulated lists of) data (the first). +\end{frame} + +\begin{frame} + \frametitle{Features} + \begin{itemize} + \item Data and Functions semantically the same + \item Natural interactive use through functional programming with + side effects + \item Batch is a simplification of interactive -- not a special mode! + \end{itemize} +\end{frame} + + \subsection{Current Approach / Implementation} + + + \begin{frame}{Computable and Executable Statistics requires} \begin{itemize} @@ -472,7 +564,7 @@ \end{itemize} \end{frame} -\begin{frame}{Common Lisp Stat.} +\begin{frame}{Common Lisp Statistics} Ross talked about Lisp. I generally agree. My current research program dates back over 3 years, and: \begin{itemize} @@ -482,20 +574,25 @@ \begin{enumerate} \item numerics redone using CFFI-based BLAS/LAPLACK (cl-blapack) \item matrix interface based on MatLisp - \item design of graphics system on-going; constraint system - (Cells) supporting interactivity. + \item starting design of a user interface system (interfaces, + visuals). \item general framework for model specification (regression, likelihood, ODEs) + \item general framework for algorithm specification (bootstrap, + MLE, algorithmic data anaylsis methods). \end{enumerate} \end{itemize} \end{frame} -\begin{frame}{Common Lisp Stat} +\begin{frame}{Common Lisp Statistics} - Source code available! - - (but it is ugly, works only in 10 cases, and changes with my moods). - + \begin{itemize} + \item Implemented using SBCL. Contributed fixes for + Clozure/OpenMCL. Goal to target CLISP + \item Supports LispStat prototype object system + \item Package-based design -- only use the components you need, or + the components whose API you like. + \end{itemize} \end{frame} \section*{Summary} @@ -508,6 +605,36 @@ % \end{enumerate} % \end{frame} + +\section{Discussion} + +\begin{frame} + \frametitle{Outlook} + \begin{itemize} + \item Semantics and Computability have captured a great deal of + attention in the informatics and business computing R\&D worlds + \item Statistically-driven Decision Making and Knowledge Discovery + is, with high likelihood, the next challenging stage after data + integration. + \item Statistical practice (theory and application) can be enhanced, + made more efficient, providing increased benefit to organizations + and groups using appropriate methods. + \item Lisp as a language, shares characteristics of both Latin + (difficult dead language useful for classical training) and German + (difficult living language useful for general life). Of course, + for some people, they are not difficult. + \end{itemize} + + The research program described in this talk is currently driving the + design of CommonLisp Stat, which leverages concepts and approaches + from the dead and moribund XLisp-Stat project. + + \url{http://repo.or.cz/w/CommonLispStat.git/} + + \url{http://www.github.com/blindglobe/} + +\end{frame} + \begin{frame}{Summary} \begin{itemize} @@ -533,292 +660,271 @@ \end{itemize} \end{frame} -% All of the following is optional and typically not needed. -\appendix +% % All of the following is optional and typically not needed. +% \appendix -\section*{\appendixname} +% \section*{\appendixname} -\begin{frame} \frametitle{Complements and Backup} - No more, stop here. Questions? (now or later). -\end{frame} -\begin{frame}{The Industrial Challenge.}{Getting the Consulting Right.} - % - A title should summarize the slide in an understandable fashion - % for anyone how does not follow everything on the slide itself. - - \begin{itemize} - \item Recording assumptions for the next data analyst, reviewer. - Use \texttt{itemize} a lot. - \item - Use very short sentences or short phrases. - \end{itemize} -\end{frame} - - -\begin{frame}{The Industrial Challenge.}{Getting the Right Research Fast.} - % - A title should summarize the slide in an understandable fashion - % for anyone how does not follow everything on the slide itself. - - \begin{itemize} - \item - Use \texttt{itemize} a lot. - \item - Use very short sentences or short phrases. - \end{itemize} -\end{frame} - - -\begin{frame}{Explicating the Work-flow}{QA/QC-based improvements.} - - -\end{frame} - -\section{Semantics and Statistical Practice} - - -\begin{frame} - \frametitle{Statistical Activity Leads to Reports} - \framesubtitle{You read what you know, do you understand it?} - - How can we improve the communication of the ideas we have? +% \begin{frame} \frametitle{Complements and Backup} +% No more, stop here. Questions? (now or later). +% \end{frame} - Precision of communication? +% \begin{frame}{The Industrial Challenge.}{Getting the Consulting Right.} +% % - A title should summarize the slide in an understandable fashion +% % for anyone how does not follow everything on the slide itself. -\end{frame} +% \begin{itemize} +% \item Recording assumptions for the next data analyst, reviewer. +% Use \texttt{itemize} a lot. +% \item +% Use very short sentences or short phrases. +% \end{itemize} +% \end{frame} +% \begin{frame}{The Industrial Challenge.}{Getting the Right Research Fast.} +% % - A title should summarize the slide in an understandable fashion +% % for anyone how does not follow everything on the slide itself. -\begin{frame} \frametitle{Communication Requires Context} - \framesubtitle{Intentions imply more than one might like...} +% \begin{itemize} +% \item +% Use \texttt{itemize} a lot. +% \item +% Use very short sentences or short phrases. +% \end{itemize} +% \end{frame} - \begin{itemize} - \item Consideration of what we might do - \item Applications with related functionality - \end{itemize} -\end{frame} +% \begin{frame}{Explicating the Work-flow}{QA/QC-based improvements.} -\begin{frame} - \frametitle{Design Patterns} - \framesubtitle{Supporting Work-flow Transitions} - - (joint work with H Wickham): The point of this research program is - not to describe what to do at any particular stage of work, but to - encourage researchers and practitioners to consider how the - translation and transfer of information between stages so that work - is not lost. +% \end{frame} - Examples of stages in a work-flow: - \begin{itemize} - \item planning, execution, reporting; - \item scoping, illustrative examples or counter examples, algorithmic construction, - article writing. - \item descriptive statistics, preliminary inferential analysis, - model/assumption checking, final inferential analysis, - communication of scientific results - \end{itemize} - Description of work-flows is essential to initiating discussions on - quality/efficiency of approaches to work. -\end{frame} +% \section{Motivation} -\section{Design Challenges} +% \subsection{IT Can Speed up Deliverables in Statistical Practice} -\begin{frame} - \frametitle{Activities are enhanced by support} +% \begin{frame}{Our Generic Work-flow and Life-cycle} +% {describing most data analytic activities} +% Workflow: +% \begin{enumerate} +% \item Scope out the problem +% \item Sketch out a potential solution +% \item Implement until road-blocks appear +% \item Deliver results +% \end{enumerate} - \begin{itemize} - \item Mathematical manipulation can be enhanced by symbolic - computation - \item Statistical programming can be enabled by examples and related - algorithm implementation - \item Datasets, to a limited extent, can self-describe. - \end{itemize} -\end{frame} +% Lifecycle: +% \begin{enumerate} +% \item paper sketch +% \item 1st e-draft of text/code/date (iterate to \#1, discarding) +% \item cycle through work +% \item publish +% \item ``throw-away'' +% \end{enumerate} +% but there is valuble information that could enable the next +% generation! +% \end{frame} -\begin{frame} - \frametitle{Executable and Computable Science} - - Use of algorithms and construction to describe how things work. +% \begin{frame}[fragile]{Paper $\rightarrow$ Computer $\rightarrow$ Article $\rightarrow$ Computer}{Cut and Paste makes for large errors.} +% \begin{itemize} +% \item Problems in a regulatory setting +% \item Regulatory issues are just ``best practices'' +% \end{itemize} - Support for agent-based approaches -\end{frame} +% Why do we ``copy/paste'', or analogously, restart our work? +% pro: +% \begin{itemize} +% \item every time we repeat, we reinforce the idea in our brain +% \item review of ideas can help improve them +% \end{itemize} +% con: +% \begin{itemize} +% \item inefficiency +% \item introduction of mistakes +% \item loss of historical context +% \item changes to earlier work (on a different development branch) +% can not propagate. +% \end{itemize} +% \end{frame} -\begin{frame} - \frametitle{What is Data? Metadata?} +% \section{Semantics and Statistical Practice} - Data: what we've observed - MetaData: context for observations, enables semantics. -\end{frame} +% \begin{frame} +% \frametitle{Statistical Activity Leads to Reports} +% \framesubtitle{You read what you know, do you understand it?} +% How can we improve the communication of the ideas we have? -\section{Discussion} +% Precision of communication? -\begin{frame} - \frametitle{Outlook} - \begin{itemize} - \item Semantics and Computability have captured a great deal of - attention in the informatics and business computing R\&D worlds - \item Statistically-driven Decision Making and Knowledge Discovery - is, with high likelihood, the next challenging stage after data - integration. - \item Statistical practice (theory and application) can be enhanced, - made more efficient, providing increased benefit to organizations - and groups using appropriate methods. -% \item Lisp as a language, shares characteristics of both Latin -% (difficult dead language useful for classical training) and German -% (difficult living language useful for general life). -% Of course, for some people, they are not difficult. - \end{itemize} +% \end{frame} - The research program described in this talk is currently driving the - design of CommonLisp Stat, which leverages concepts and approaches - from the dead and moribund XLisp-Stat project. - \url{http://repo.or.cz/w/CommonLispStat.git/} -\end{frame} -\section{Common Lisp Statistics} +% \begin{frame} \frametitle{Communication Requires Context} +% \framesubtitle{Intentions imply more than one might like...} -\begin{frame} - \frametitle{Interactive Programming} - \framesubtitle{Everything goes back to being Lisp-like} - \begin{itemize} - \item Interactive programming (as originating with Lisp): works - extremely well for data analysis (Lisp being the original - ``programming with data'' language). - \item Theories/methods for how to do this are reflected in styles - for using R. - \end{itemize} -\end{frame} +% \begin{itemize} +% \item Consideration of what we might do +% \item Applications with related functionality +% \end{itemize} +% \end{frame} -\begin{frame}[fragile] - \frametitle{Lisp} - Lisp (LISt Processor) is different than most high-level computing - languages, and is very old (1956). Lisp is built on lists of things - which are evaluatable. -\begin{verbatim} -(functionName data1 data2 data3) -\end{verbatim} - or ``quoted'': -\begin{verbatim} -'(functionName data1 data2 data3) -\end{verbatim} - which is shorthand for -\begin{verbatim} -(list functionName data1 data2 data3) -\end{verbatim} - The difference is important -- lists of data (the second/third) are - not (yet?!) functions applied to (unencapsulated lists of) data (the first). -\end{frame} -\begin{frame} - \frametitle{Features} - \begin{itemize} - \item Data and Functions semantically the same - \item Natural interactive use through functional programming with - side effects - \item Batch is a simplification of interactive -- not a special mode! - \end{itemize} -\end{frame} +% \begin{frame} +% \frametitle{Design Patterns} +% \framesubtitle{Supporting Work-flow Transitions} + +% (joint work with H Wickham): The point of this research program is +% not to describe what to do at any particular stage of work, but to +% encourage researchers and practitioners to consider how the +% translation and transfer of information between stages so that work +% is not lost. +% Examples of stages in a work-flow: +% \begin{itemize} +% \item planning, execution, reporting; +% \item scoping, illustrative examples or counter examples, algorithmic construction, +% article writing. +% \item descriptive statistics, preliminary inferential analysis, +% model/assumption checking, final inferential analysis, +% communication of scientific results +% \end{itemize} +% Description of work-flows is essential to initiating discussions on +% quality/efficiency of approaches to work. +% \end{frame} +% \section{Design Challenges} -% \begin{frame}[fragile] -% \frametitle{Defining Variables} -% \framesubtitle{Setting variables} -% \begin{verbatim} -% (setq ) -% \end{verbatim} -% Example: -% \begin{verbatim} -% (setq ess-source-directory -% "/home/rossini/R-src") -% \end{verbatim} -% \end{frame} +% \begin{frame} +% \frametitle{Activities are enhanced by support} -% \begin{frame}[fragile] -% \frametitle{Defining on the fly} -% \begin{verbatim} -% (setq ess-source-directory -% (lambda () (file-name-as-directory -% (expand-file-name -% (concat (default-directory) -% ess-suffix "-src"))))) -% \end{verbatim} -% (Lambda-expressions are anonymous functions, i.e. ``instant-functions'') +% \begin{itemize} +% \item Mathematical manipulation can be enhanced by symbolic +% computation +% \item Statistical programming can be enabled by examples and related +% algorithm implementation +% \item Datasets, to a limited extent, can self-describe. +% \end{itemize} % \end{frame} +% \begin{frame} +% \frametitle{Executable and Computable Science} + +% Use of algorithms and construction to describe how things work. -% \begin{frame}[fragile] -% \frametitle{Function Reuse} -% By naming the function, we could make the previous example reusable -% (if possible): -% \begin{verbatim} -% (defun my-src-directory () -% (file-name-as-directory -% (expand-file-name -% (concat (default-directory) -% ess-suffix "-src")))) -% \end{verbatim} -% Example: -% \begin{verbatim} -% (setq ess-source-directory (my-src-directory)) -% \end{verbatim} +% Support for agent-based approaches % \end{frame} % \begin{frame} -% \frametitle{Equality Among Packages} -% \begin{itemize} -% \item more/less equal can be described specifically through -% overriding imports. -% \end{itemize} +% \frametitle{What is Data? Metadata?} + +% Data: what we've observed + +% MetaData: context for observations, enables semantics. % \end{frame} -\subsection*{For Further Reading} -\begin{frame}[allowframebreaks] - \frametitle{Related Material} + +% % \begin{frame}[fragile] +% % \frametitle{Defining Variables} +% % \framesubtitle{Setting variables} +% % \begin{verbatim} +% % (setq ) +% % \end{verbatim} +% % Example: +% % \begin{verbatim} +% % (setq ess-source-directory +% % "/home/rossini/R-src") +% % \end{verbatim} +% % \end{frame} + +% % \begin{frame}[fragile] +% % \frametitle{Defining on the fly} +% % \begin{verbatim} +% % (setq ess-source-directory +% % (lambda () (file-name-as-directory +% % (expand-file-name +% % (concat (default-directory) +% % ess-suffix "-src"))))) +% % \end{verbatim} +% % (Lambda-expressions are anonymous functions, i.e. ``instant-functions'') +% % \end{frame} + + +% % \begin{frame}[fragile] +% % \frametitle{Function Reuse} +% % By naming the function, we could make the previous example reusable +% % (if possible): +% % \begin{verbatim} +% % (defun my-src-directory () +% % (file-name-as-directory +% % (expand-file-name +% % (concat (default-directory) +% % ess-suffix "-src")))) +% % \end{verbatim} +% % Example: +% % \begin{verbatim} +% % (setq ess-source-directory (my-src-directory)) +% % \end{verbatim} +% % \end{frame} + + +% % \begin{frame} +% % \frametitle{Equality Among Packages} +% % \begin{itemize} +% % \item more/less equal can be described specifically through +% % overriding imports. +% % \end{itemize} +% % \end{frame} + + +% \subsection*{For Further Reading} + +% \begin{frame}[allowframebreaks] +% \frametitle{Related Material} - \begin{thebibliography}{10} +% \begin{thebibliography}{10} - \beamertemplatebookbibitems - % Start with overview books. +% \beamertemplatebookbibitems +% % Start with overview books. - \bibitem{LispStat1990} - L.~Tierney - \newblock {\em LispStat}. +% \bibitem{LispStat1990} +% L.~Tierney +% \newblock {\em LispStat}. - \beamertemplatearticlebibitems - % Followed by interesting articles. Keep the list short. +% \beamertemplatearticlebibitems +% % Followed by interesting articles. Keep the list short. - \bibitem{Rossini2001} - AJ.~Rossini - \newblock Literate Statistical Practice - \newblock {\em Proceedings of the Conference on Distributed - Statistical Computing}, 2001. +% \bibitem{Rossini2001} +% AJ.~Rossini +% \newblock Literate Statistical Practice +% \newblock {\em Proceedings of the Conference on Distributed +% Statistical Computing}, 2001. - \bibitem{RossiniLeisch2003} - AJ.~Rossini and F.~Leisch - \newblock Literate Statistical Practice - \newblock {\em Technical Report Series, University of Washington - Department of Biostatistics}, 2003. +% \bibitem{RossiniLeisch2003} +% AJ.~Rossini and F.~Leisch +% \newblock Literate Statistical Practice +% \newblock {\em Technical Report Series, University of Washington +% Department of Biostatistics}, 2003. - \beamertemplatearrowbibitems - % Followed by interesting articles. Keep the list short. +% \beamertemplatearrowbibitems +% % Followed by interesting articles. Keep the list short. - \bibitem{CLS} - Common Lisp Stat, 2008. - \newblock \url{http://repo.or.cz/CommonLispStat.git/} +% \bibitem{CLS} +% Common Lisp Stat, 2008. +% \newblock \url{http://repo.or.cz/CommonLispStat.git/} - \end{thebibliography} -\end{frame} +% \end{thebibliography} +% \end{frame} \end{document} -- 2.11.4.GIT