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Knitr: Elegant, flexible and fast dynamic report generation with R

Knitr: Elegant, flexible and fast dynamic report generation with R
Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more). This package is developed on GitHub; for installation instructions and FAQ’s, see README. Motivation One of the difficulties with extending Sweave is we have to copy a large amount of code from the utils package (the file SweaveDrivers.R has more than 700 lines of R code), and this is what the two packages mentioned above have done. Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to humans what we want the computer to do. – Donald E. Features Acknowledgements Misc

Doxygen Generate documentation from source code Doxygen is the de facto standard tool for generating documentation from annotated C++ sources, but it also supports other popular programming languages such as C, Objective-C, C#, PHP, Java, Python, IDL (Corba, Microsoft, and UNO/OpenOffice flavors), Fortran, VHDL, Tcl, and to some extent D. Doxygen can help you in three ways: It can generate an on-line documentation browser (in HTML) and/or an off-line reference manual (in ) from a set of documented source files. There is also support for generating output in RTF (MS-Word), PostScript, hyperlinked PDF, compressed HTML, and Unix man pages. Doxygen is developed under Mac OS X and Linux, but is set-up to be highly portable. Doxygen license Copyright © 1997-2016 by Dimitri van Heesch. Permission to use, copy, modify, and distribute this software and its documentation under the terms of the GNU General Public License is hereby granted. Sponsored links(not related to doxygen)

Software - Miquel De Cáceres Ainsa Indicspecies R package Indicator species are species that are used as ecological indicators of community or habitat types, environmental conditions, or environmental changes. In order to determine indicator species, the characteristic to be predicted is represented in the form of a classification of the sites, which is compared to the patterns of distribution of the species found in that set of sites. 'Indicspecies' is an R package that contains a set of functions to assess the strength of relationship between species and a classification of sites. Download indicspecies (ver. 1.6.7) from CRAN. Authors: Miquel De Cáceres, Florian Jansen Classifications of vegetation provide a way of summarizing our knowledge of vegetation patterns. Resniche R package The niche concept underlies most ecological questions, from population growth and geographic expansion to community dynamics and ecosystem functioning. Authors: Miquel De Cáceres STI R package Beals smoothing R functions Vegana package

TikZ and PGF examples Home > TikZ > Examples TikZ and PGF examples Welcome to the PGF and TikZ examples gallery. Browse by: Features | Tags | Technical areas | Non-technical areas |Authors Navigation Subscribe to the TikZ examples RSS feed Recently added examples Random city [PDF] [TEX] [Open in Overleaf] Circumscribed Parallelepiped [PDF] [TEX] [Open in Overleaf] Poincare Diagram, Classification of Phase Portraits [PDF] [TEX] [Open in Overleaf] Excised, Horizon-Penetrating Coordinates for Black Hole Spacetime [PDF] [TEX] [Open in Overleaf] Show all examples | Show in chronological order | Show as list Features Tags Scientific and technical areas Other areas Authors about | contact | Impressum

Statistics with R Warning Here are the notes I took while discovering and using the statistical environment R. However, I do not claim any competence in the domains I tackle: I hope you will find those notes useful, but keep you eyes open -- errors and bad advice are still lurking in those pages... Should you want it, I have prepared a quick-and-dirty PDF version of this document. The old, French version is still available, in HTML or as a single file. You may also want all the code in this document. 1. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Emacs による構造化編集技法 Emacs の構造化編集技法による DocBook や他の SGML/XML 標準の初心者向けの入門 Imass Alejandro [FAMILY Given] 作者: SATOH Satoru [FAMILY Given] この文書は Emacs で SGML マークアップやその派生(例えば、XML や HTML)を編集することに焦点をあてています。 私は最近 LyX で Docbook 編集を試してみて、こちらのアプローチも多くの将来性を持ち、 多くのユーザーの WYSIWYG から構造化文書の技法へ移行を容易にすることができると思いました。 この文書は SGML、Docbook、Emacs の入門を意図したものではありません。 また Docbook、SGML そして XML は急速に変化していっていて、多くのトレンドがあります。 私はワープロの時代に生まれたので、5 年ぐらい前に Unix と troff を発見するまで組版について知る機会がまったくありませんでした。 もしかしたら、プログラマとしての私には自然なことだったかもしれません。 私はそれほど長くはない文書やマニュアルについての作業でMicrosoft Word™を使う受難に酷く苦しめられてきました。 文書の形式(スタイル)は統合が困難。 とにかく、What You See Is What You Get(WYSIWYG)ツールが企業と人々に何年にもわたって強いてきたすべての問題について 述べてみました。 まとめると、WYSIWYG ``ワープロ''は非常に小さなオフィスや単純かつ私的な作業には素晴しいツールであるということです。 WYSIWYG ツールは初心者には相対的には易しいけれども、高度な使い方をしようと思うと、学習曲線はとても険しいものとなり、 ツールに依存した標準を押し付けられることとなります。 これらのツールで非常に重要な文書を扱わなければならなくなった人ならきっと誰でも同意すると思いますが、 それは何か間違っていて、そういった仕事をこなすには何らかの代替手段があるべきなんです。 私は ``テキストプロセッシング(処理)'' という言葉を ``ワードプロセッシング''とは区別し、Unix の最初の本から学んだのと同じ意味で使っています。 テキスト処理は文書を要素に分けます: 例 1. <! 4.2.3. 対

Common Concepts in Statistics [M.Tevfik DORAK] Genetics Population Genetics Genetic Epidemiology Bias & Confounding Evolution HLA MHC Homepage M.Tevfik Dorak, MD, PhD Please use this address next time: See also Common Terms in Mathematics; Statistical Analysis in HLA & Disease Association Studies; Epidemiology (incl. Genetic Epidemiology Glossary) For more LINKS, see the end of this page [Please note that the best way to find an entry is to use the Find option from the Edit menu, or CTRL + F] Absolute risk: Probability of an event over a period of time; expressed as a cumulative incidence like 10-year risk of 10% (meaning 10% of individuals in the group of interest will develop the condition in the next 10 year period). Accuracy: The degree to which a parameter (like the mean) is immune systematic error or bias. Addition rule: The probability of any of one of several mutually exclusive events occurring is equal to the sum of their individual probabilities. ANCOVA: See covariance models.

Ecological Models and Data in R This is the web site for a book published by Princeton University Press (ISBN 0691125228). It is available from Princeton University Press and Amazon.com. Data and scripts for labs: Other data and scripts: Most of the data for the book are available in the emdbook package on CRAN. Most of the R code for doing things in the book is now in the two packages bbmle (also available in a development version) and emdbook, both available from R archive (CRAN) or via install.packages from inside R. Other miscellaneous R code: pdfhtmlxmlRnwR Warning: everything below here may be somewhat out of date ...If you want to see the existing notes for the course, start here. Old PDFs An old draft: 3 August 2007 (PDF, 6 MB). Individual chapters Last update: 27 December 2006 Formats: PDF, Rnw (Sweave -- original "source code"), R (R code only)

Rtips. Revival 2012! Paul E. Johnson <pauljohn @ ku.edu> The original Rtips started in 1999. You are reading the New Thing! The first chore is to cut out the old useless stuff that was no good to start with, correct mistakes in translation (the quotation mark translations are particularly dangerous, but also there is trouble with ~, $, and -. (I thought it was cute to call this “StatsRus” but the Toystore’s lawyer called and, well, you know…) If you need a tip sheet for R, here it is. This is not a substitute for R documentation, just a list of things I had trouble remembering when switching from SAS to R. Heed the words of Brian D. 1.1 Bring raw numbers into R (05/22/2012) This is truly easy. myDataFrame <- read.table(‘‘myData’’,header=TRUE) If you type “? Suppose you have tab delimited data with blank spaces to indicate “missing” values. myDataFrame<-read.table("myData",sep="\t",na.strings=" ",header=TRUE) Be aware than anybody can choose his/her own separator. 1.2 Basic notation on data access (12/02/2012) or ?

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