Data Mining with Rattle and R
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Tinn-R Team Has a New Member Working on the Source Code: Wel- Come Huashan Chen
Editus eBook Series Editus eBooks is a series of electronic books aimed at students and re- searchers of arts and sciences in general. Tinn-R Editor (2010 1. ed. Rmetrics) Tinn-R Editor - GUI forR Language and Environment (2014 2. ed. Editus) José Cláudio Faria Philippe Grosjean Enio Galinkin Jelihovschi Ricardo Pietrobon Philipe Silva Farias Universidade Estadual de Santa Cruz GOVERNO DO ESTADO DA BAHIA JAQUES WAGNER - GOVERNADOR SECRETARIA DE EDUCAÇÃO OSVALDO BARRETO FILHO - SECRETÁRIO UNIVERSIDADE ESTADUAL DE SANTA CRUZ ADÉLIA MARIA CARVALHO DE MELO PINHEIRO - REITORA EVANDRO SENA FREIRE - VICE-REITOR DIRETORA DA EDITUS RITA VIRGINIA ALVES SANTOS ARGOLLO Conselho Editorial: Rita Virginia Alves Santos Argollo – Presidente Andréa de Azevedo Morégula André Luiz Rosa Ribeiro Adriana dos Santos Reis Lemos Dorival de Freitas Evandro Sena Freire Francisco Mendes Costa José Montival Alencar Junior Lurdes Bertol Rocha Maria Laura de Oliveira Gomes Marileide dos Santos de Oliveira Raimunda Alves Moreira de Assis Roseanne Montargil Rocha Silvia Maria Santos Carvalho Copyright©2015 by JOSÉ CLÁUDIO FARIA PHILIPPE GROSJEAN ENIO GALINKIN JELIHOVSCHI RICARDO PIETROBON PHILIPE SILVA FARIAS Direitos desta edição reservados à EDITUS - EDITORA DA UESC A reprodução não autorizada desta publicação, por qualquer meio, seja total ou parcial, constitui violação da Lei nº 9.610/98. Depósito legal na Biblioteca Nacional, conforme Lei nº 10.994, de 14 de dezembro de 2004. CAPA Carolina Sartório Faria REVISÃO Amek Traduções Dados Internacionais de Catalogação na Publicação (CIP) T591 Tinn-R Editor – GUI for R Language and Environment / José Cláudio Faria [et al.]. – 2. ed. – Ilhéus, BA : Editus, 2015. xvii, 279 p. ; pdf Texto em inglês. -
A Word from Our 2006 Section Chairs
VOLUME 17, NO 1, JUNE 2006 A joint newsletter of the Statistical Computing & Statistical Graphics Sections of the American Statistical Association A Word from our 2006 Section Chairs PAUL MURRELL STEPHAN R. SAIN GRAPHICS COMPUTING I would like to begin by When Carey Priebe asked me highlighting a couple of to run for one of the section interesting recent offices a couple of years ago, I developments in the area of wasn’t exactly sure what I was Statistical Graphics. getting into. Now that I have There has been a lot of been chair for a couple of activity on the GGobi project months, I’m still not totally lately, with an updated web sure what I’ve gotten into. site, new versions, and The one thing I do know, improved links to R. I though, is that I’m very happy encourage you to (re)visit www.ggobi.org and see what to be involved in the section. they’ve been up to. There are a lot of very interesting things going on and, as always, a lot of opportunity for people who have an The third volume of the Handbook of Computational interest in statistical computing. Statistics, which is focused on Data Visualization, is scheduled for publication at the end of this year and Continues on Page 2.......... there will be a workshop as a satellite of Compstat 2006. This important volume will contain over 30 contributions and will provide a comprehensive overview of all areas of data visualization. Information about this project is available at gap.stat.sinica.edu.tw/ HBCSC. -
The Rattle Package September 30, 2007
The rattle Package September 30, 2007 Type Package Title A graphical user interface for data mining in R using GTK Version 2.2.64 Date 2007-09-29 Author Graham Williams <[email protected]> Maintainer Graham Williams <[email protected]> Depends R (>= 2.2.0) Suggests RGtk2, ada, amap, arules, bitops, cairoDevice, cba, combinat, doBy, e1071, ellipse, fEcofin, fCalendar, fBasics, foreign, fpc, gdata, gtools, gplots, Hmisc, kernlab, MASS, Matrix, mice, network, pmml, randomForest, rggobi, ROCR, RODBC, rpart, RSvgDevice, XML Description Rattle provides a Gnome (RGtk2) based interface to R functionality for data mining. The aim is to provide a simple and intuitive interface that allows a user to quickly load data from a CSV file (or via ODBC), transform and explore the data, and build and evaluate models, and export models as PMML (predictive modelling markup language). All of this with knowing little about R. All R commands are logged and available for the user, as a tool to then begin interacting directly with R itself, if so desired. Rattle also exports a number of utility functions and the graphical user interface does not need to be run to deploy these. License GPL version 2 or newer URL http://rattle.togaware.com/ R topics documented: audit . 2 calcInitialDigitDistr . 3 calculateAUC . 3 centers.hclust . 4 drawTreeNodes . 5 drawTreesAda . 6 evaluateRisk . 7 1 2 audit genPlotTitleCmd . 8 rattle_gui . 9 listRPartRules . 9 listTreesAda . 10 plotBenfordsLaw . 11 plotNetwork . 11 plotOptimalLine . 12 plotRisk . 14 printRandomForests . 16 randomForest2Rules . 17 rattle . 18 savePlotToFile . 19 treeset.randomForest . 19 Index 21 audit Sample dataset for illustration Rattle functionality. -
Uros2018.Pdf
Use of R in O cial Statistics 6th International Conference 2018 2018OV010 Eventbanner uRos2018 Rolbanner 100x200_DEF OPTIES .indd 1 23-7-2018 09:58:34 Eventbanner uRos2018 1920x400.jpg Eventbanner uRos2018 1920x400.bb Welcome The global community of R users is growing, and the number of Naonal and Interna- onal Stascal Offices that are adopng R is growing as well. About five years ago, when this conference was organized as an internaonal conference for the first me in Romania, we felt a bit like outlaws using Free and Open Source Soware (FOSS) in an area where commercial packages rule the land. How mes have changed: in the mean me FOSS, and in parcular R is considered a driving force of innovaon in academia, industry and government. The popularity of R is demonstrated by the hundreds of local R user groups, the thousands of R packages, and the RConsorum. The current conference, at Stascs Netherlands, marks the first occasion outside of the place where it was conceived: Romania. We are therefore especially pleased that our keynote speakers have roots in both countries. Alina Matei is a professor of stascs in Switzerland with Romanian roots. She will talk about opmal sample coordinaon using R. An important topic in mes where the reducon of response burden and increasing nonresponse rates force us to use smaller, more complex sampling methods. Not many R users are aware that there is a ‘touch of Dutch’ in R. Since 2017, Jeroen Ooms (UC Berkeley) is the maintainer of both Rtools and R for Windows. He will tell us about what it takes to compile, release, and modernize a system on which more than 12,500 R packages and millions of users rely every day. -
Statcharrms R Version Installation Guide
StatCharrms R Version Installation Guide 2014-07-14 Written and Programmed By: Joe Swintek, BTS Based off StatCharrms SAS version developed by: Dr. John Green, DuPont Applied Statistics Group, Stine-Haskell Research Center Additional Testing By: Kevin Flynn, USEPA Jon Haselman, USEPA Funded By: USEPA Under Contract EP-D-13-052 Installation StatCharrms is a graphical user interface front end for R, designed for ease of operation that performs the recommended statistical procedure used in the Medaka Extended One Generation Test (MEOGRT) and Larval Amphibian Growth and Development Assay (LAGDA). The statistical procedures implemented within StatCharrms are; the Rao-Scott adjusted Cochran-Armitage trend test by slices (RSCABS), a repeated measures ANVOA using time and treatment as fixed effects, Jonckheere-Terpstra trend test, Dunnett test, Kruskal Wallis, Dunns Test, one way ANOVA, weighted one way ANOVA, mixed effect ANOVA for imbalanced replicate structures, and a mixed effect Cox proportional model for imbalanced replicate structures. StatCharrms is implemented as an R workspace preloaded with the required functions. To Start StatCharrms double click on the R icon labeled StatCharrms-V##.RData. Now the installation of the required packages can begin by typing : Install.StatCharrms() into the R console and then hitting enter. R is case sensitive so you will need to type the command exactly as it is above. Figure one shows what is should look like. Executing the installation command will, by default, create a folder on the C drive called “RLib” that will contain the libraries needed for StatCharrms to run. Figure 1: Next a window asking to select CRAN (Comprehensive R Archive Network) mirror will popup. -
Sexy-Rgtk: a Package for Programming Rgtk2 GUI in a User-Friendly Manner
sexy-rgtk: a package for programming RGtk2 GUI in a user-friendly manner Damien Lerouxa and Nathalie Villa-Vialaneixa;b a INRA, UR875, MIAT F-31326 Castanet Tolosan - France [email protected] b SAMM, Université Paris 1 F-75634 Paris - France [email protected] Keywords: Gtk2, RGtk2, GUI There are many dierent ways to program Graphical User Interfaces (GUI) in R.[1] provides an overview of the available methods, describing ways to program R GUI with RGtk2, qtbase and tcltk. More recently, the package shiny, for building interactive web applications, was also released (the rst version has been published on December, 2012). The package RGtk2 [2] is probably one of the most complete packages to program complex and highly customizable GUI. It is based on GTK2 (the GIMP Toolkit, http://www.gtk. org/), which is a multi-platform toolkit for creating Graphical User Interfaces. GTK2 oers a complete set of widgets and can be used to develop complete application suites working on Linux, Windows and Mac OS X. Although very exible, each RGtk2 interface results in a long script that has a counterintuitive syntax for most R users. For instance, the simple window of Figure1 1 is obtained with the command lines provided in Figure2 (left). Figure 1: A simple GUI interface made with RGtk2. One attempt to overcome the diculty of the RGtk2 syntax is the package gWidgets but, quoting its reference manual The excellent RGtk2 package opens up the full power of the GTK2 toolkit, only a fraction of which is available though gWidgetsRGtk2. -
Pipenightdreams Osgcal-Doc Mumudvb Mpg123-Alsa Tbb
pipenightdreams osgcal-doc mumudvb mpg123-alsa tbb-examples libgammu4-dbg gcc-4.1-doc snort-rules-default davical cutmp3 libevolution5.0-cil aspell-am python-gobject-doc openoffice.org-l10n-mn libc6-xen xserver-xorg trophy-data t38modem pioneers-console libnb-platform10-java libgtkglext1-ruby libboost-wave1.39-dev drgenius bfbtester libchromexvmcpro1 isdnutils-xtools ubuntuone-client openoffice.org2-math openoffice.org-l10n-lt lsb-cxx-ia32 kdeartwork-emoticons-kde4 wmpuzzle trafshow python-plplot lx-gdb link-monitor-applet libscm-dev liblog-agent-logger-perl libccrtp-doc libclass-throwable-perl kde-i18n-csb jack-jconv hamradio-menus coinor-libvol-doc msx-emulator bitbake nabi language-pack-gnome-zh libpaperg popularity-contest xracer-tools xfont-nexus opendrim-lmp-baseserver libvorbisfile-ruby liblinebreak-doc libgfcui-2.0-0c2a-dbg libblacs-mpi-dev dict-freedict-spa-eng blender-ogrexml aspell-da x11-apps openoffice.org-l10n-lv openoffice.org-l10n-nl pnmtopng libodbcinstq1 libhsqldb-java-doc libmono-addins-gui0.2-cil sg3-utils linux-backports-modules-alsa-2.6.31-19-generic yorick-yeti-gsl python-pymssql plasma-widget-cpuload mcpp gpsim-lcd cl-csv libhtml-clean-perl asterisk-dbg apt-dater-dbg libgnome-mag1-dev language-pack-gnome-yo python-crypto svn-autoreleasedeb sugar-terminal-activity mii-diag maria-doc libplexus-component-api-java-doc libhugs-hgl-bundled libchipcard-libgwenhywfar47-plugins libghc6-random-dev freefem3d ezmlm cakephp-scripts aspell-ar ara-byte not+sparc openoffice.org-l10n-nn linux-backports-modules-karmic-generic-pae -
Package 'Rattle'
Package ‘rattle’ September 5, 2017 Type Package Title Graphical User Interface for Data Science in R Version 5.1.0 Date 2017-09-04 Depends R (>= 2.13.0) Imports stats, utils, ggplot2, grDevices, graphics, magrittr, methods, RGtk2, stringi, stringr, tidyr, dplyr, cairoDevice, XML, rpart.plot Suggests pmml (>= 1.2.13), bitops, colorspace, ada, amap, arules, arulesViz, biclust, cba, cluster, corrplot, descr, doBy, e1071, ellipse, fBasics, foreign, fpc, gdata, ggdendro, ggraptR, gplots, grid, gridExtra, gtools, gWidgetsRGtk2, hmeasure, Hmisc, kernlab, Matrix, mice, nnet, odfWeave, party, playwith, plyr, psych, randomForest, rattle.data, RColorBrewer, readxl, reshape, rggobi, ROCR, RODBC, rpart, SnowballC, survival, timeDate, tm, verification, wskm, RGtk2Extras, xgboost Description The R Analytic Tool To Learn Easily (Rattle) provides a Gnome (RGtk2) based interface to R functionality for data science. The aim is to provide a simple and intuitive interface that allows a user to quickly load data from a CSV file (or via ODBC), transform and explore the data, build and evaluate models, and export models as PMML (predictive modelling markup language) or as scores. All of this with knowing little about R. All R commands are logged and commented through the log tab. Thus they are available to the user as a script file or as an aide for the user to learn R or to copy-and-paste directly into R itself. Rattle also exports a number of utility functions and the graphical user interface, invoked as rattle(), does not need to be run to deploy these. License -
The R Journal Volume 3/2, December 2011
The Journal Volume 3/2, December 2011 A peer-reviewed, open-access publication of the R Foundation for Statistical Computing Contents Editorial..................................................3 Contributed Research Articles Creating and Deploying an Application with (R)Excel and R...................5 glm2: Fitting Generalized Linear Models with Convergence Problems.............. 12 Implementing the Compendium Concept with Sweave and DOCSTRIP............. 16 Watch Your Spelling!........................................... 22 Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming 29 Nonparametric Goodness-of-Fit Tests for Discrete Null Distributions............... 34 Using the Google Visualisation API with R.............................. 40 GrapheR: a Multiplatform GUI for Drawing Customizable Graphs in R............. 45 rainbow: An R Package for Visualizing Functional Time Series.................. 54 Programmer’s Niche Portable C++ for R Packages...................................... 60 News and Notes R’s Participation in the Google Summer of Code 2011....................... 64 Conference Report: useR! 2011..................................... 68 Forthcoming Events: useR! 2012.................................... 70 Changes in R............................................... 72 Changes on CRAN............................................ 84 News from the Bioconductor Project.................................. 86 R Foundation News........................................... 87 2 The Journal is a peer-reviewed publication -
December 2016, Volume 34 No.2
ISSN 0735-1348 Department of Physics, East Carolina University, 1000 East 5th Street, Greenville, NC 27858, USA http://www.ecu.edu/cs-cas/physics/ancient-timeline/ December 2016, Volume 34 No.2 IRSL dating of fast-fading sanidine feldspars from Sulawesi, Indonesia 1 Bo Li, Richard G. Roberts, Adam Brumm, Yu-Jie Guo, Budianto Hakim, Muhammad Ramli, Maxime Aubert, Rainer Grün, Jian-xin Zhao, E. Wahyu Saptomo Bayesian statistics in luminescence dating: The ‘baSAR’-model and its 14 implementation in the R package ‘Luminescence’ Norbert Mercier, Sebastian Kreutzer, Claire Christophe, Guillaume Guérin, Pierre Guibert, Christelle Lahaye, Philippe Lanos, Anne Philippe, and Chantal Tribolo RLumShiny - A graphical user interface for the R Package ‘Luminescence’ 22 Christoph Burow, Sebastian Kreutzer, Michael Dietze, Margret C. Fuchs, Manfred Fischer, Christoph Schmidt, Helmut Brückner Thesis abstracts 33 Bibliography 36 Ancient TL Started by the late David Zimmerman in 1977 EDITOR Regina DeWitt, Department of Physics, East Carolina University, Howell Science Complex, 1000 E. 5th Street Greenville, NC 27858, USA; Tel: +252-328-4980; Fax: +252-328-0753 ([email protected]) EDITORIAL BOARD Ian K. Bailiff, Luminescence Dating Laboratory, Univ. of Durham, Durham, UK ([email protected]) Geoff A.T. Duller, Institute of Geography and Earth Sciences, Aberystwyth University, Ceredigion, Wales, UK ([email protected]) Sheng-Hua Li, Department of Earth Sciences, The University of Hong Kong, Hong Kong, China ([email protected]) Shannon Mahan, U.S. Geological Survey, Denver Federal Center, Denver, CO, USA ([email protected]) Richard G. Roberts, School of Earth and Environmental Sciences, University of Wollongong, Australia ([email protected]) REVIEWERS PANEL Richard M. -
Outline of Machine Learning
Outline of machine learning The following outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of computer science[1] (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions. Contents What type of thing is machine learning? Branches of machine learning Subfields of machine learning Cross-disciplinary fields involving machine learning Applications of machine learning Machine learning hardware Machine learning tools Machine learning frameworks Machine learning libraries Machine learning algorithms Machine learning methods Dimensionality reduction Ensemble learning Meta learning Reinforcement learning Supervised learning Unsupervised learning Semi-supervised learning Deep learning Other machine learning methods and problems Machine learning research History of machine learning Machine learning projects Machine learning organizations Machine learning conferences and workshops Machine learning publications -
John W. Emerson: Curriculum Vitae
John W. Emerson Yale University Phone: (203) 432-0638 Department of Statistics and Data Science Email: [email protected] 24 Hillhouse Avenue, New Haven, CT 06511 Homepage: http://www.stat.yale.edu/∼jay/ Education Ph.D. Statistics, Yale University, 1994-2002. Dissertation: Asymptotic Admissibility and Bayesian Estima- tion. Advisor: John Hartigan, Eugene Higgins Professor of Statistics. M.Phil. Economics, Oxford University, 1992-1994. Thesis: Finite-Sample Properties of Sample Selection Estimators: Principles and a Case Study. Topics of Study: Micro and Macroeconomic Theory, Industrial Organization, and Econometrics. B.A. Economics and Mathematics, Williams College, 1988-1992. Honors Thesis in Mathematics: Bayesian Categorical Data Analysis with Multivariate Qualitative Measurements. Academic Appointments Professor Adjunct and Director of Graduate Studies, Department of Statistics and Data Science, Yale University, 2016-present Yale-NUS Contributing Faculty, 2014-present Associate Professor Adjunct and Director of Graduate Studies, Department of Statistics, Yale Univer- sity, 2012-2016 Visiting Professor, Peking University, Summers 2007, 2009, 2014, 2017 Guest Professor, National Taipei University of Technology, Summer 2012 Associate Professor, Department of Statistics, Yale University, 2009-2012 Faculty Affiliate, Yale Center for Environment Law and Policy, 2006-present Assistant Professor, Department of Statistics, Yale University, 2003-2009 Lecturer, Department of Statistics, Yale University, 2003 Graduate Instructor for special