ESS — Emacs Speaks Statistics
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Emacspeak — the Complete Audio Desktop User Manual
Emacspeak | The Complete Audio Desktop User Manual T. V. Raman Last Updated: 19 November 2016 Copyright c 1994{2016 T. V. Raman. All Rights Reserved. Permission is granted to make and distribute verbatim copies of this manual without charge provided the copyright notice and this permission notice are preserved on all copies. Short Contents Emacspeak :::::::::::::::::::::::::::::::::::::::::::::: 1 1 Copyright ::::::::::::::::::::::::::::::::::::::::::: 2 2 Announcing Emacspeak Manual 2nd Edition As An Open Source Project ::::::::::::::::::::::::::::::::::::::::::::: 3 3 Background :::::::::::::::::::::::::::::::::::::::::: 4 4 Introduction ::::::::::::::::::::::::::::::::::::::::: 6 5 Installation Instructions :::::::::::::::::::::::::::::::: 7 6 Basic Usage. ::::::::::::::::::::::::::::::::::::::::: 9 7 The Emacspeak Audio Desktop. :::::::::::::::::::::::: 19 8 Voice Lock :::::::::::::::::::::::::::::::::::::::::: 22 9 Using Online Help With Emacspeak. :::::::::::::::::::: 24 10 Emacs Packages. ::::::::::::::::::::::::::::::::::::: 26 11 Running Terminal Based Applications. ::::::::::::::::::: 45 12 Emacspeak Commands And Options::::::::::::::::::::: 49 13 Emacspeak Keyboard Commands. :::::::::::::::::::::: 361 14 TTS Servers ::::::::::::::::::::::::::::::::::::::: 362 15 Acknowledgments.::::::::::::::::::::::::::::::::::: 366 16 Concept Index :::::::::::::::::::::::::::::::::::::: 367 17 Key Index ::::::::::::::::::::::::::::::::::::::::: 368 Table of Contents Emacspeak :::::::::::::::::::::::::::::::::::::::::: 1 1 Copyright ::::::::::::::::::::::::::::::::::::::: -
Emacs Speaks Statistics (ESS): a Multi-Platform, Multi-Package Intelligent Environment for Statistical Analysis
Emacs Speaks Statistics (ESS): A multi-platform, multi-package intelligent environment for statistical analysis A.J. Rossini Richard M. Heiberger Rodney A. Sparapani Martin Machler¨ Kurt Hornik ∗ Date: 2003/10/22 17:34:04 Revision: 1.255 Abstract Computer programming is an important component of statistics research and data analysis. This skill is necessary for using sophisticated statistical packages as well as for writing custom software for data analysis. Emacs Speaks Statistics (ESS) provides an intelligent and consistent interface between the user and software. ESS interfaces with SAS, S-PLUS, R, and other statistics packages under the Unix, Microsoft Windows, and Apple Mac operating systems. ESS extends the Emacs text editor and uses its many features to streamline the creation and use of statistical software. ESS understands the syntax for each data analysis language it works with and provides consistent display and editing features across packages. ESS assists in the interactive or batch execution by the statistics packages of statements written in their languages. Some statistics packages can be run as a subprocess of Emacs, allowing the user to work directly from the editor and thereby retain a consistent and constant look- and-feel. We discuss how ESS works and how it increases statistical programming efficiency. Keywords: Data Analysis, Programming, S, SAS, S-PLUS, R, XLISPSTAT,STATA, BUGS, Open Source Software, Cross-platform User Interface. ∗A.J. Rossini is Research Assistant Professor in the Department of Biostatistics, University of Washington and Joint Assis- tant Member at the Fred Hutchinson Cancer Research Center, Seattle, WA, USA mailto:[email protected]; Richard M. -
An Introduction to ESS + Xemacs for Windows Users of R
An Introduction to ESS + XEmacs for Windows Users of R John Fox∗ McMaster University Revised: 23 January 2005 1WhyUseESS+XEmacs? Emacs is a powerful and widely used programmer’s editor. One of the principal attractions of Emacs is its programmability: Emacs can be adapted to provide customized support for program- ming languages, including such features as delimiter (e.g., parenthesis) matching, syntax highlight- ing, debugging, and version control. The ESS (“Emacs Speaks Statistics”) package provides such customization for several common statistical computing packages and programming environments, including the S family of languages (R and various versions of S-PLUS), SAS, and Stata, among others. The current document introduces the use of ESS with R running under Microsoft Windows. For some Unix/Linux users, Emacs is more a way of life than an editor: It is possible to do almost everything from within Emacs, including, of course, programming, but also writing documents in mark-up languages such as HTML and LATEX; reading and writing email; interacting with the Unix shell; web browsing; and so on. I expect that this kind of generalized use of Emacs will not be attractive to most Windows users, who will prefer to use familiar, and specialized, tools for most of these tasks. There are several versions of the Emacs editor, the two most common of which are GNU Emacs, a product of the Free Software Foundation, and XEmacs, an offshoot of GNU Emacs. Both of these implementations are free and are available for most computing platforms, including Windows. In my opinion, based only on limited experience, XEmacs offers certain advantages to Windows users, such as easier installation, and so I will explain the use of ESS under this version of the Emacs editor.1 As I am writing this, there are several Windows programming editors that have been customized for use with R. -
XLISP-STAT a Statistical Environment Based on the XLISP Language (Version 2.0)
I XLISP-STAT A Statistical Environment Based on the XLISP Language (Version 2.0) by Luke Tierney l.5i1 University of Minnesota School of Statistics Technical Report Number 528 July 1989 Contents Preface .. 3 1 Starting and Finishing 6 2 Introduction to Basics 8 2.1 Data ........ 8 2.2 The Listener and the Evaluator . 8 3 Elementary Statistical Operations 11 3.1 First Steps ......... 11 3.2 Summary Statistics and Plots 12 3.3 Two Dimensional Plots 16 3.4 Plotting Functions ..... 19 4 More on Generating and Modifying Data 20 4.1 Generating Random Data . 20 4.2 Generating Systematic Data . 20 4.3 Forming Subsets and .Deleting Cases 21 4.4 Combining Several Lists 22 4.5 Modifying Data . 22 5 Some Useful Shortcuts 24 5.1 Getting Help . 24 5.2 Listing and Undefining Variables .. 26 5.3 More on the XLISP-STAT Listener .. 26 5 .4 Loading Files . 28 5.5 Saving Your Work ..... 28 5.6 The XLISP-STAT Editor 29 5.7 Reading Data Files .. 29 5.8 User Initialization File 29 6 More Elaborate Plots 30 6.1 Spinning Plots . ..... 30 6.2 Scatterplot Matrices • It ••••• 32 6.3 Interacting with Individual Plots 35 6.4 Linked Plots ....... 35 6.5 Modifying a Scatter Plot . 36 6.6 Dynamic Simulations . 39 7 Regression 42 8 Defining Your Own Functions and Methods 47 8.1 Defining Functions .... 47 8.2 Anonymous Functions .. 48 8.3 Some Dynamic Simulations 48 8.4 Defining Methods . 51 8.5 Plot Methods . 52 9 Matrices and Arrays 53 10 Nonlinear Regression 54 1 11 One Way ANOVA 57 12 Maximization and Maximum Likeliho~d Estimation 58 13 Approximate Bayesian Computations 61 A XLISP-STAT on UNIX Systems 68 A.1 XLISP-STAT Under the X11 Window System. -
ESS — Emacs Speaks Statistics
ESS — Emacs Speaks Statistics ESS version 5.14 The ESS Developers (A.J. Rossini, R.M. Heiberger, K. Hornik, M. Maechler, R.A. Sparapani, S.J. Eglen, S.P. Luque and H. Redestig) Current Documentation by The ESS Developers Copyright c 2002–2010 The ESS Developers Copyright c 1996–2001 A.J. Rossini Original Documentation by David M. Smith Copyright c 1992–1995 David M. Smith Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Chapter 1: Introduction to ESS 1 1 Introduction to ESS The S family (S, Splus and R) and SAS statistical analysis packages provide sophisticated statistical and graphical routines for manipulating data. Emacs Speaks Statistics (ESS) is based on the merger of two pre-cursors, S-mode and SAS-mode, which provided support for the S family and SAS respectively. Later on, Stata-mode was also incorporated. ESS provides a common, generic, and useful interface, through emacs, to many statistical packages. It currently supports the S family, SAS, BUGS/JAGS, Stata and XLisp-Stat with the level of support roughly in that order. A bit of notation before we begin. emacs refers to both GNU Emacs by the Free Software Foundation, as well as XEmacs by the XEmacs Project. The emacs major mode ESS[language], where language can take values such as S, SAS, or XLS. -
Using GNU Make to Manage the Workflow of Data Analysis Projects
JSS Journal of Statistical Software June 2020, Volume 94, Code Snippet 1. doi: 10.18637/jss.v094.c01 Using GNU Make to Manage the Workflow of Data Analysis Projects Peter Baker University of Queensland Abstract Data analysis projects invariably involve a series of steps such as reading, cleaning, summarizing and plotting data, statistical analysis and reporting. To facilitate repro- ducible research, rather than employing a relatively ad-hoc point-and-click cut-and-paste approach, we typically break down these tasks into manageable chunks by employing sep- arate files of statistical, programming or text processing syntax for each step including the final report. Real world data analysis often requires an iterative process because many of these steps may need to be repeated any number of times. Manually repeating these steps is problematic in that some necessary steps may be left out or some reported results may not be for the most recent data set or syntax. GNU Make may be used to automate the mundane task of regenerating output given dependencies between syntax and data files. In addition to facilitating the management of and documenting the workflow of a complex data analysis project, such automation can help minimize errors and make the project more reproducible. It is relatively simple to construct Makefiles for small data analysis projects. As projects increase in size, difficulties arise because GNU Make does not have inbuilt rules for statistical and related software. Without such rules, Makefiles can become unwieldy and error-prone. This article addresses these issues by providing GNU Make pattern rules for R, Sweave, rmarkdown, SAS, Stata, Perl and Python to streamline management of data analysis and reporting projects. -
Abkürzungs-Liste ABKLEX
Abkürzungs-Liste ABKLEX (Informatik, Telekommunikation) W. Alex 1. Juli 2021 Karlsruhe Copyright W. Alex, Karlsruhe, 1994 – 2018. Die Liste darf unentgeltlich benutzt und weitergegeben werden. The list may be used or copied free of any charge. Original Point of Distribution: http://www.abklex.de/abklex/ An authorized Czechian version is published on: http://www.sochorek.cz/archiv/slovniky/abklex.htm Author’s Email address: [email protected] 2 Kapitel 1 Abkürzungen Gehen wir von 30 Zeichen aus, aus denen Abkürzungen gebildet werden, und nehmen wir eine größte Länge von 5 Zeichen an, so lassen sich 25.137.930 verschiedene Abkür- zungen bilden (Kombinationen mit Wiederholung und Berücksichtigung der Reihenfol- ge). Es folgt eine Auswahl von rund 16000 Abkürzungen aus den Bereichen Informatik und Telekommunikation. Die Abkürzungen werden hier durchgehend groß geschrieben, Akzente, Bindestriche und dergleichen wurden weggelassen. Einige Abkürzungen sind geschützte Namen; diese sind nicht gekennzeichnet. Die Liste beschreibt nur den Ge- brauch, sie legt nicht eine Definition fest. 100GE 100 GBit/s Ethernet 16CIF 16 times Common Intermediate Format (Picture Format) 16QAM 16-state Quadrature Amplitude Modulation 1GFC 1 Gigabaud Fiber Channel (2, 4, 8, 10, 20GFC) 1GL 1st Generation Language (Maschinencode) 1TBS One True Brace Style (C) 1TR6 (ISDN-Protokoll D-Kanal, national) 247 24/7: 24 hours per day, 7 days per week 2D 2-dimensional 2FA Zwei-Faktor-Authentifizierung 2GL 2nd Generation Language (Assembler) 2L8 Too Late (Slang) 2MS Strukturierte -
Spatial Tools for Econometric and Exploratory Analysis
Spatial Tools for Econometric and Exploratory Analysis Michael F. Goodchild University of California, Santa Barbara Luc Anselin University of Illinois at Urbana-Champaign http://csiss.org Outline ¾A Quick Tour of a GIS ¾Spatial Data Analysis ¾CSISS Tools Spatial Data Analysis Principles: 1. Integration ¾Linking data through common location the layer cake ¾Linking processes across disciplines spatially explicit processes e.g. economic and social processes interact at common locations 2. Spatial analysis ¾Social data collected in cross- section longitudinal data are difficult to construct ¾Cross-sectional perspectives are rich in context can never confirm process though they can perhaps falsify useful source of hypotheses, insights 3. Spatially explicit theory ¾Theory that is not invariant under relocation ¾Spatial concepts (location, distance, adjacency) appear explicitly ¾Can spatial concepts ever explain, or are they always surrogates for something else? 4. Place-based analysis ¾Nomothetic - search for general principles ¾Idiographic - description of unique properties of places ¾An old debate in Geography The Earth's surface ¾Uncontrolled variance ¾There is no average place ¾Results depend explicitly on bounds ¾Places as samples ¾Consider the model: y = a + bx Tract Pop Location Shape 1 3786 x,y 2 2966 x,y 3 5001 x,y 4 4983 x,y 5 4130 x,y 6 3229 x,y 7 4086 x,y 8 3979 x,y Iij = EiAjf (dij) / ΣkAkf (dik) Aj d Ei ij Types of Spatial Data Analysis ¾ Exploratory Spatial Data Analysis • exploring the structure of spatial data • determining -
Paquetes Estadísticos Con Licencia Libre (I) Free Statistical
2013, Vol. 18 No 2, pp. 12-33 http://www.uni oviedo.es/reunido /index.php/Rema Paquetes estadísticos con licencia libre (I) Free statistical software (I) Carlos Carleos Artime y Norberto Corral Blanco Departamento de Estadística e I. O. y D.M.. Universidad de Oviedo RESUMEN El presente artículo, el primero de una serie de trabajos, presenta un panorama de los principales paquetes estadísticos disponibles en la actualidad con licencia libre, entendiendo este concepto a partir de los grandes proyectos informáticos así autodefinidos que han cristalizado en los últimos treinta años. En esta primera entrega se presta atención fundamentalmente al programa R. Palabras clave : paquetes estadísticos, software libre, R. ABSTRACT This article, the first in a series of works, presents an overview of the major statistical packages available today with free license, understanding this concept from the large computer and self-defined projects that have crystallized in the last thirty years. In this first paper we pay attention mainly to the program R. Keywords : statistical software, free software, R. Contacto: Norberto Corral Blanco Email: [email protected] . 12 Revista Electrónica de Metodología Aplicada (2013), Vol. 18 No 2, pp. 12-33 1.- Introducción La palabra “libre” es usada, y no siempre de manera adecuada, en múltiples campos de conocimiento así que la informática no iba a ser una excepción. La palabra es especialmente problemática porque su traducción al inglés, “free”, es aun más polisémica, e incluye el concepto “gratis”. En 1985 se fundó la Free Software Foundation (FSF) para defender una definición rigurosa del sófguar libre. La propia palabra “sófguar” (del inglés “software”, por oposición a “hardware”, quincalla, aplicado al soporte físico de la informática) es en sí misma problemática. -
High Dimensional Data Analysis Via the SIR/PHD Approach
High dimensional data analysis via the SIR/PHD approach April 6, 2000 Preface Dimensionality is an issue that can arise in every scientific field. Generally speaking, the difficulty lies on how to visualize a high dimensional function or data set. This is an area which has become increasingly more important due to the advent of computer and graphics technology. People often ask : “How do they look?”, “What structures are there?”, “What model should be used?” Aside from the differences that underly the various scientific con- texts, such kind of questions do have a common root in Statistics. This should be the driving force for the study of high dimensional data analysis. Sliced inverse regression(SIR) and principal Hessian direction(PHD) are two basic di- mension reduction methods. They are useful for the extraction of geometric information underlying noisy data of several dimensions - a crucial step in empirical model building which has been overlooked in the literature. In this Lecture Notes, I will review the theory of SIR/PHD and describe some ongoing research in various application areas. There are two parts. The first part is based on materials that have already appeared in the literature. The second part is just a collection of some manuscripts which are not yet published. They are included here for completeness. Needless to say, there are many other high dimensional data analysis techniques (and we may encounter some of them later on) that should deserve more detailed treatment. In this complex field, it would not be wise to anticipate the existence of any single tool that can outperform all others in every practical situation. -
Pl a N a Ss E Ss De V Elo P E X P Lo R E R E S E a Rc H B U Ild C O N Ta C T P
PLAN ACTION VERBS Use consistent verb tense (generally past tense). Start phrases with descriptive action verbs. Supply quantitative data whenever possible. Adapt terminology to include key words. Incorporate action verbs with keywords and current “hot” topics, programs, tools, testing terms, and instrumentation to develop concise, yet highly descriptive phrases. Remember that résumés are scanned for such words, so do everything possible to incorporate important phraseology and current keywords into your résumé. ASSESS From What Color is Your Parachute, Richard Bolles, 2005 achieved delivered founded motivated resolved adapted detailed gathered navigated responded analyzed detected generated operated restored arbitrated determined guided perceived retrieved DEVELOP ascertained devised hypothesized persuaded reviewed assessed diagnosed identified piloted risked attained discovered illustrated predicted scheduled audited displayed implemented problem-solved selected built dissected improvised proofread served collected distributed influenced projected shaped conceptualized diverted initiated promoted summarized EXPLORE compiled eliminated innovated publicized supplied computed enforced inspired purchased surveyed conducted established installed reasoned synthesized conserved evaluated integrated recommended taught consolidated examined investigated referred tested constructed expanded maintained rehabilitated transcribed consulted experimented mediated rendered trouble-shot controlled expressed mentored reported tutored RESEARCH counseled -
ESS — Emacs Speaks Statistics
ESS | Emacs Speaks Statistics ESS version 5.3.6 The ESS Developers (A.J. Rossini, R.M. Heiberger, K. Hornik, M. Maechler, R.A. Sparapani and S.J. Eglen) Current Documentation by The ESS Developers Copyright c 2002{2006 The ESS Developers Copyright c 1996{2001 A.J. Rossini Original Documentation by David M. Smith Copyright c 1992{1995 David M. Smith Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Chapter 1: Introduction to ESS 1 1 Introduction to ESS The S family (S, Splus and R) and SAS statistical analysis packages provide sophisticated statistical and graphical routines for manipulating data. Emacs Speaks Statistics (ESS) is based on the merger of two pre-cursors, S-mode and SAS-mode, which provided support for the S family and SAS respectively. Later on, Stata-mode was also incorporated. ESS provides a common, generic, and useful interface, through emacs, to many statistical packages. It currently supports the S family, SAS, BUGS, Stata and XLisp-Stat with the level of support roughly in that order. A bit of notation before we begin. emacs refers to both GNU Emacs by the Free Software Foundation, as well as XEmacs by the XEmacs Project. The emacs major mode ESS[language], where language can take values such as S, SAS, or XLS.