DOCSLIB.ORG

Basic R for Finance

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Basic R for Finance Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 Basic R for Finance Diethelm Würtz Longhow Lam Andrew Ellis Yohan Chalabi Rmetrics Association & Finance Online Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 R/Rmetrics eBook Series R/Rmetrics eBooks is a series of electronic books and user guides aimed at students and practitioner who use R/Rmetrics to analyze financial markets. A Discussion of Time Series Objects for R in Finance (2009) Diethelm Würtz, Yohan Chalabi, Andrew Ellis Portfolio Optimization with R/Rmetrics (2010), Diethelm Würtz, William Chen, Yohan Chalabi, Andrew Ellis Basic R for Finance (2010), Diethelm Würtz, Yohan Chalabi, Longhow Lam, Andrew Ellis Early Bird Edition Financial Market Data for R/Rmetrics (2010) Diethelm Würtz, Andrew Ellis, Yohan Chalabi Early Bird Edition Indian Financial Market Data for R/Rmetrics (2010) Diethelm Würtz, Mahendra Mehta, Andrew Ellis, Yohan Chalabi R/Rmetrics Workshop Singapore 2010 (2010) Diethelm Würtz, Mahendra Mehta, David Scott, Juri Hinz Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 BASIC R FOR FINANCE DIETHELM WÜRTZ LONGHOW LAM ANDREW ELLIS YOHAN CHALABI RMETRICS ASSOCIATION &FINANCE ONLINE Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 Series Editors: PD Dr. Diethelm Würtz Dr. Martin Hanf Institute of Theoretical Physics and Finance Online GmbH Curriculum for Computational Science Weinbergstrasse 41 Swiss Federal Institute of Technology 8006 Zurich Hönggerberg, HIT K 32.2 8093 Zurich Publisher: Contact Address: Finance Online GmbH Rmetrics Association Swiss Information Technologies Weinbergstrasse 41 Weinbergstrasse 41 8006 Zurich 8006 Zurich [email protected] Authors: Cover Design & Photography: Yohan Chalabi, ETH Zurich Chris Eckert Andrew Ellis, Rmetrics Association Leaf dtp production Longhow Lam, ABN AMRO Diethelm Würtz, ETH Zurich ISBN: eISBN: DOI: © 2009-2010, Finance Online GmbH, Zurich All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Finance Online GmbH) except for brief excerpts in con- nection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. Limit of Liability/Disclaimer of Warranty: While the publisher and authors have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 DEDICATION This book is dedicated to all those who have helped make Rmetrics what it is today: The leading open source software environment in computational finance and financial engineering. Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 PREFACE R und Rmetrics are the talk of the town. The statistical software package R is one of the most promising tools for rapid prototyping of financial applications. The userR conferences and Rmetrics Meielisalp workshops reflect the growing interest in R und Rmetrics. Have ever thought of giving R a try, using one of the many packages, or even writing your own functions and learning the programming language? If only the initial learning curve weren’t so steep. This is where "Basic R for Finance" can help you: You will learn the basics of programming in R, and how to implement your models and applications. You will not only create graphics, you will also learn how to customize them them to suit your needs. You will learn how to write your own programs, how to write efficient and optimized code. Furthermore, you will be assisted by a multi- tude of very detailed case studies, ranging from computing skewness and kurtosis statistics to designing and optimizing portfolios of assets. This book is divided into two several thematically distinct parts: the first four parts give an introduction to R, and focus on the following topics: computions, programming, plotting and statistics and inference. Parts five, six, svem and eight contain a collection of case studies from topics such as utility functions, asset management, option valuation, and portfo- lio design. We hope you enjoy this book! Diethelm Würtz Zurich, July 2010 vii Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 CONTENTS DEDICATION V PREFACE VII CONTENTS IX LISTOF FIGURES XV LISTOF TABLES XVII I Computations 1 1 DATA STRUCTURES 3 1.1 Vectors ...................................... 3 1.2 Matrices ..................................... 6 1.3 Arrays ....................................... 9 1.4 Data Frames .................................. 10 1.5 Time Series ................................... 12 1.6 Lists ........................................ 14 1.7 Printing the Object Structure ....................... 18 2 DATA MANIPULATION 19 2.1 Manipulating Vectors ............................ 19 2.2 Manipulating Matrices ........................... 24 2.3 Manipulating Data Frames ....................... 28 2.4 Working with Attributes .......................... 34 2.5 Manipulating Character Strings .................... 36 2.6 Creating Factors from Continuous Data . 40 3 IMPORTING AND EXPORTING DATA 43 3.1 Writing to Text Files ............................. 43 3.2 Reading from a Text File with scan() . 44 3.3 Reading from a Text File with readLines() . 45 ix Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 X CONTENTS 3.4 Reading from a Text File with read.table() . 46 3.5 Importing Example Data Files ..................... 49 3.6 Importing Historical Data Sets from the Internet . 50 4 OBJECT TYPES 51 4.1 Characterization of Objects ........................ 51 4.2 Double ...................................... 52 4.3 Integers ...................................... 57 4.4 Complex ..................................... 60 4.5 Logical ...................................... 61 4.6 Missing Data .................................. 62 4.7 Character .................................... 63 4.8 NULL ....................................... 63 II Programming 65 5 WRITING FUNCTIONS 67 5.1 Writing your first function ........................ 67 5.2 Arguments and Variables ......................... 69 5.3 Scoping rules .................................. 72 5.4 Lazy evaluation ................................ 73 5.5 Flow control .................................. 74 6 DEBUGGING YOUR R FUNCTIONS 79 6.1 The traceback() function ........................ 79 6.2 The function warning() and stop() . 80 6.3 Stepping Through a Function ...................... 81 6.4 The function browser() .......................... 82 7 EFFICIENT CALCULATIONS 83 7.1 Vectorized Computations ......................... 83 7.2 The Family of apply() Functions ................... 86 7.3 The function by() .............................. 88 7.4 The Function outer() ........................... 89 8 USING S3 AND S4 CLASSES 91 8.1 S3 Class Model Basics ............................ 91 8.2 S4 Class Model Basics ............................ 96 9 R PACKAGES 97 9.1 Base R packages ................................ 97 9.2 Contributed R Packages from CRAN . 98 9.3 R Packages under Development from R-forge . 98 9.4 R Package Usage ............................... 98 Owner: DAWSON FRANCE ([email protected]) @ 109.2.242.246 CONTENTS XI 9.5 Package Management Functions .................... 99 III Plotting 101 10 HIGH LEVEL PLOTS 103 10.1 Scatter Plots ...................................103 10.2 Line Plots .....................................104 10.3 More about the plot() Function . 106 10.4 Distribution Plots ...............................107 10.5 Pie and Bar Plots ...............................110 10.6 Stars- and Segments Plots . 112 10.7 Bi- and Multivariate Plots . 114 11 CUSTOMIZING PLOTS 117 11.1 More About Plot Function Arguments . 117 11.2 Graphical Parameters ............................120 11.3 Margins, Plot and Figure Regions . 123 11.4 More About Colours .............................126 11.5 Adding Graphical Elements to an Existing Plot . 128 11.6 Controlling the Axes .............................132 12 GRAPHICAL DEVICES 135 12.1 Available Devices ...............................135 12.2 Device Management under Windows . 135 12.3 List of device functions . 137 IV Statistics and Inference 139 13 BASIC STATISTICAL FUNCTIONS 141 13.1 Statistical Summaries ............................141 13.2 Distribution Functions . 143 13.3 Random Numbers ..............................145 13.4 Hypothesis Testing ..............................146 13.5 Parameter Estimation . 148 13.6 Distribution tails and quantiles . 149 14 LINEAR TIME SERIES ANALYSIS 151 14.1 Overview of Functions
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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
    [Show full text]
  • ECON 18: Quantitative Equity Analysis Winter Study 2009
    ECON 18: Quantitative Equity Analysis Winter Study 2009 Professor David Kane dkane at iq.harvard.edu Teaching Fellow David Phillips '11 David.E.Phillips at williams.edu Overview This class will introduce students to applied quantitative equity research. We will briefly review the history and approach of academic research in equity pricing via a reading of selected papers. Students will then learn the best software tools for conducting such research. Students will work as teams to replicate the results of a published academic paper and then extend those results in a non-trivial manner. This course is designed for two types of students: first, those interested in applied economic research, and second, those curious about how that research is used and evaluated by finance professionals. See "Publication, ​ Publication" for useful background reading on my approach. Special thanks to Bill Alpert, ​ Senior Editor at Barron's, Joe Masters '96, Assistant Professor of Mathematics, SUNY Buffalo, George Poulogiannis and Dan Gerlanc '07 for agreeing to participate in the class. Prerequisites and Background Reading There are no formal prerequisites for the class. Students who have taken STAT 201 will have an advantage in terms of their exposure to R, the programming language that we use. ​ ​ But other students will be able to learn enough R both before class starts and in the first ​ ​ week of Winter Study. You can learn more about and download R. See Wikipedia for an ​ ​ ​ ​ overview of R. Read An Introduction to R. ​ ​ You are welcome to use whatever editor you like for writing code, but I strongly recommend Emacs.
    [Show full text]
  • Introduction to R for Finance
    Computational Finance and Risk Management mm 40 60 80 100 120 40 Introduction to R Guy Yollin 60 Principal Consultant, r-programming.org Affiliate Instructor, University of Washington 80 Guy Yollin (Copyright © 2011) Introduction to R 1 / 132 Outline 1 R language references mm 40 60 80 100 120 2 R overview and history 3 R language and environment basics 4 The working directory, data files, and data manipulation 40 5 Basic statistics and the normal distribution 6 Basic plotting 7 Working with time series in R 60 8 Variable scoping in R 9 The R help system 10 Web resources80 for R 11 IDE editors for R Guy Yollin (Copyright © 2011) Introduction to R 2 / 132 Outline 1 R language references mm 40 60 80 100 120 2 R overview and history 3 R language and environment basics 4 The working directory, data files, and data manipulation 40 5 Basic statistics and the normal distribution 6 Basic plotting 7 Working with time series in R 60 8 Variable scoping in R 9 The R help system 10 Web resources80 for R 11 IDE editors for R Guy Yollin (Copyright © 2011) Introduction to R 4 / 132 Essential web resources mm 40 60 80 100 120 An Introduction to R R Reference Card W.N. Venables, D.M. Smith Tom Short R Development Core Team 40 R Reference Card cat(..., file="", sep=" ") prints the arguments after coercing to Slicing and extracting data character; sep is the character separator between arguments Indexing lists by Tom Short, EPRI Solutions, Inc., [email protected] 2005-07-12 print(a, ...) prints its arguments; generic, meaning it can have differ- x[n] list with elements n th Granted to the public domain.
    [Show full text]
  • Timedate Package by Yohan Chalabi, Martin Mächler, and Diethelm Würtz R, See Ripley & Hornik(2001) and Grothendieck & Petzoldt(2004)
    CONTRIBUTED RESEARCH ARTICLES 19 Rmetrics - timeDate Package by Yohan Chalabi, Martin Mächler, and Diethelm Würtz R, see Ripley & Hornik(2001) and Grothendieck & Petzoldt(2004). Another problem commonly faced in managing time and dates is the midnight standard. Indeed, the problem can be handled in different ways depending on the standard in use. For example, the standard C library does not allow for the “midnight” standard in the format “24:00:00” (Bateman(2000)). However, the timeDate package supports this format. Moreover, timeDate includes functions for cal- endar manipulations, business days, weekends, and public and ecclesiastical holidays. One can handle Figure 1: World map with major time zones. 1 day count conventions and rolling business conven- tions. Such periods can pertain to, for example, the last working day of the month. The below examples Abstract The management of time and holidays illustrate this point. can prove crucial in applications that rely on his- In the remaining part of this note, we first present torical data. A typical example is the aggregation the structure of the "timeDate" class. Then, we ex- of a data set recorded in different time zones and plain the creation of "timeDate" objects. The use of under different daylight saving time rules. Be- financial centers with DST rules is described next. sides the time zone conversion function, which is Thereafter, we explain the management of holidays. well supported by default classes in R, one might Finally, operations on "timeDate" objects such as need functions to handle special days or holi- mathematical operations, rounding, subsetting, and days. In this respect, the package timeDate en- coercions are discussed.
    [Show full text]
  • Package 'Fbasics'
    Package ‘fBasics’ March 7, 2020 Title Rmetrics - Markets and Basic Statistics Date 2017-11-12 Version 3042.89.1 Author Diethelm Wuertz [aut], Tobias Setz [cre], Yohan Chalabi [ctb] Martin Maechler [ctb] Maintainer Tobias Setz <[email protected]> Description Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management. Depends R (>= 2.15.1), timeDate, timeSeries Imports stats, grDevices, graphics, methods, utils, MASS, spatial, gss, stabledist ImportsNote akima not in Imports because of non-GPL licence. Suggests akima, RUnit, tcltk LazyData yes License GPL (>= 2) Encoding UTF-8 URL https://www.rmetrics.org NeedsCompilation yes Repository CRAN Date/Publication 2020-03-07 11:06:10 UTC 1 2 R topics documented: R topics documented: fBasics-package . .4 acfPlot . 13 akimaInterp . 15 baseMethods . 17 BasicStatistics . 18 BoxPlot . 19 characterTable . 20 colorLocator . 20 colorPalette . 21 colorTable . 25 colVec . 25 correlationTest . 26 decor . 28 distCheck . 29 DistributionFits . 29 fBasics-deprecated . 31 fBasicsData . 32 fHTEST . 34 getS4 . 35 gh.............................................. 36 ghFit . 38 ghMode . 40 ghMoments . 41 ghRobMoments . 42 ghSlider . 43 ght.............................................. 44 ghtFit .
    [Show full text]
  • Fat Tail Analysis and Package Fattailsr
    Fat Tail Analysis and Package FatTailsR [email protected] 9th R/Rmetrics Workshop 27June2015 Villa Hatt – Zürich [email protected] Fat Tail Analysis and Package FatTailsR 9th R/Rmetrics Workshop 27June2015 1 / 26 Outline 1 Introduction 2 Mathematics 3 Fat tails: Application in finance 4 Conclusion [email protected] Fat Tail Analysis and Package FatTailsR 9th R/Rmetrics Workshop 27June2015 2 / 26 About InModelia Since 2009: a small company located in Paris Consulting services in data analysis Design of experiments Multivariate nonlinear modeling Neural networks Times series Software development in R June 2014: 8th R/Rmetrics workshop First version of R package FatTailsR introducing Kiener distributions with symmetric (3 parameters) and asymmetric (4 parameters) fat tails June 2015: 9th R/Rmetrics workshop Second version of R package FatTailsR including 3 functions for parameter estimation R package FatTailsRplot including advanced plotting functions [email protected] Fat Tail Analysis and Package FatTailsR 9th R/Rmetrics Workshop 27June2015 3 / 26 Fat tails It is now well established that financial markets exhibit fat tails. It occurs on all markets. SP500 janv. 1957 − déc. 2013 SP500 janv. 1957 − déc. 2013 100 µ F(X) > 0.995 14349 points ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●● ●● ● ● ● X − 14350 points daily ●●●●●●●●●●●●●● 1.0 ●●●●●●●● ●●●●●● ●●●● ● ●●● ●●●●●●●● ●●●●● ● ●● ● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●● ●●●●●●●●● ●●● ●●●● ●● 2975 points weekly ● ●●
    [Show full text]
  • Kurt Hornik I
    R FAQ Frequently Asked Questions on R Version 2.6.2007-10-22 ISBN 3-900051-08-9 Kurt Hornik i Table of Contents 1 Introduction............................... 1 1.1 Legalese .................................................... 1 1.2 Obtaining this document..................................... 1 1.3 Citing this document ........................................ 1 1.4 Notation.................................................... 1 1.5 Feedback ................................................... 2 2 R Basics .................................. 3 2.1 What is R? ................................................. 3 2.2 What machines does R run on?............................... 3 2.3 What is the current version of R?............................. 4 2.4 How can R be obtained? ..................................... 4 2.5 How can R be installed? ..................................... 4 2.5.1 How can R be installed (Unix) ........................... 4 2.5.2 How can R be installed (Windows) ....................... 5 2.5.3 How can R be installed (Macintosh) ...................... 5 2.6 Are there Unix binaries for R? ............................... 6 2.7 What documentation exists for R? ............................ 6 2.8 Citing R .................................................... 8 2.9 What mailing lists exist for R? ............................... 9 2.10 What is CRAN? ........................................... 10 2.11 Can I use R for commercial purposes? ...................... 10 2.12 Why is R named R? ....................................... 11 2.13 What
    [Show full text]
  • Kurt Hornik I
    R faq Frequently Asked Questions on R Version 2.7.2008-04-18 ISBN 3-900051-08-9 Kurt Hornik i Table of Contents 1 Introduction ............................... 1 1.1 Legalese ................................................ 1 1.2 Obtaining this document................................. 1 1.3 Citing this document .................................... 1 1.4 Notation................................................ 1 1.5 Feedback ............................................... 2 2 R Basics .................................. 3 2.1 What is R? ............................................. 3 2.2 What machines does R run on?........................... 3 2.3 What is the current version of R?......................... 4 2.4 How can R be obtained? ................................. 4 2.5 How can R be installed? ................................. 4 2.5.1 How can R be installed (Unix) ................... 4 2.5.2 How can R be installed (Windows) ............... 5 2.5.3 How can R be installed (Macintosh).............. 5 2.6 Are there Unix binaries for R? ........................... 6 2.7 What documentation exists for R? ........................ 7 2.8 Citing R................................................ 8 2.9 What mailing lists exist for R? ........................... 9 2.10 What is cran? ....................................... 10 2.11 Can I use R for commercial purposes? .................. 11 2.12 Why is R named R? ................................... 11 2.13 What is the R Foundation? ............................ 11 3 R and S .................................
    [Show full text]
  • R Packages Installed on ITS Machines
    R Packages Installed on ITS machines Bruce Dudek Fall Semester 2020 ITS classrooms have R/RStudio installed on Windows machines. This document contains a list of all packages available in those installations. Users who wish to have additional packages installed can contact bdudek at albany dot edu and the installation can happen rapidly. Pls note that the base system R packages are listed at the bottom of the table, below the zoo package. PackageName PackageTitle abd The Analysis of Biological Data abind Combine Multidimensional Arrays ACCLMA ACC & LMA Graph Plotting acepack ACE and AVAS for Selecting Multiple Regression Transformations ada The R Package Ada for Stochastic Boosting addinmanager RStudio ’addin’ Manager additivityTests Additivity Tests in the Two Way Anova with Single Sub-class Numbers ade4 Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences ade4TkGUI ’ade4’ Tcl/Tk Graphical User Interface adegenet Exploratory Analysis of Genetic and Genomic Data adegraphics An S4 Lattice-Based Package for the Representation of Multivariate Data adehabitat Analysis of Habitat Selection by Animals adehabitatLT Analysis of Animal Movements adehabitatMA Tools to Deal with Raster Maps adephylo Exploratory Analyses for the Phylogenetic Comparative Method ads Spatial Point Patterns Analysis AED What the package does (short line) AER Applied Econometrics with R afex Analysis of Factorial Experiments agricolae Statistical Procedures for Agricultural Research airports Data on Airports akima Interpolation of Irregularly
    [Show full text]
  • Tinn-R Editor
    Tinn-R Editor José Cláudio Faria Philippe Grosjean Enio Galinkin Jelihovschi Ricardo Pietrobon Rmetrics Association R/Rmetrics eBook Series R/Rmetrics eBooks is a series of electronic books and user guides aimed at students and practitioner who use R/Rmetrics to analyze financial markets. A Discussion of Time Series Objects for R in Finance (2009) Diethelm Würtz, Yohan Chalabi, Andrew Ellis Portfolio Optimization with R/Rmetrics (2010), Diethelm Würtz, William Chen, Yohan Chalabi, Andrew Ellis Basic R for Finance (2010), Diethelm Würtz, Yohan Chalabi, Longhow Lam, Andrew Ellis Early Bird Edition Financial Market Data for R/Rmetrics (2010) Diethelm Würtz, Andrew Ellis, Yohan Chalabi Early Bird Edition Indian Financial Market Data for R/Rmetrics (2010) Diethelm Würtz, Mahendra Mehta, Andrew Ellis, Yohan Chalabi R/Rmetrics Workshop Singapore 2010 (2010) Diethelm Würtz, Mahendra Mehta, David Scott, Juri Hinz Tinn-R Editor (2010 1 ed., 2011 2 ed.) José Cláudio Faria, Philippe Grosjean, Enio Galinkin Jelihovschi, Ricardo Pietrobon Asian Option Pricing with R/Rmetrics (2010) Diethelm Würtz TINN-R EDITOR JOSÉ CLÁUDIO FARIA PHILIPPE GROSJEAN ENIO GALINKIN JELIHOVSCHI RICARDO PIETROBON RMETRICS ASSOCIATION Series Editors: Prof. Dr. Diethelm Würtz Dr. Martin Hanf Institute of Theoretical Physics and Finance Online GmbH Curriculum for Computational Science Zeltweg 7 Swiss Federal Institute of Technology 8032 Zurich Hönggerberg, HIT G 32.3 8093 Zurich Contact Address: Publisher: Rmetrics Association Rmetrics Association Zeltweg 7 Zeltweg 7 8032 Zurich 8032 Zurich [email protected] Authors: José Cláudio Faria Philippe Grosjean Enio Galinkin Jelihovschi Cover Page: Ricardo Pietrobon Camila de Godoy Teixeira ISBN: 978-3-906041-07-0 © 2010, Tinn-R for the eBook content, and Rmetrics Association, Zurich, for the the layout.
    [Show full text]