DOCSLIB.ORG

R Packages Installed on ITS Machines

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read 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 and Regularly Spaced Data AlgDesign Algorithmic Experimental Design alphahull Generalization of the Convex Hull of a Sample of Points in the Plane alr3 Data to Accompany Applied Linear Regression 3rd Edition alr4 Data to Accompany Applied Linear Regression 4th Edition amap Another Multidimensional Analysis Package AMCP A Model Comparison Perspective Amelia A Program for Missing Data anacor Simple and Canonical Correspondence Analysis animation A Gallery of Animations in Statistics and Utilities to Create Animations AnnotationDbi Annotation Database Interface ape Analyses of Phylogenetics and Evolution aplpack Another Plot Package: ’Bagplots’, ’Iconplots’, ’Summaryplots’, Slider Functions and Others argparse Command Line Optional and Positional Argument Parser 1 arm Data Analysis Using Regression and Multilevel/Hierarchical Models arules Mining Association Rules and Frequent Itemsets arulesViz Visualizing Association Rules and Frequent Itemsets asbio A Collection of Statistical Tools for Biologists ascii Export R objects to several markup languages ash David Scott’s ASH Routines askpass Safe Password Entry for R, Git, and SSH aspace A collection of functions for estimating centrographic statistics and computational geometries for spatial point patterns assertthat Easy Pre and Post Assertions AUC Threshold independent performance measures for probabilistic classifiers. automap Automatic interpolation package av Working with Audio and Video in R backports Reimplementations of Functions Introduced Since R-3.0.0 barcode Barcode distribution plots base64 Base64 Encoder and Decoder base64enc Tools for base64 encoding BayesFactor Computation of Bayes Factors for Common Designs bayesm Bayesian Inference for Marketing/Micro-Econometrics bayesplot Plotting for Bayesian Models bayestestR Understand and Describe Bayesian Models and Posterior Distributions BayesX R Utilities Accompanying the Software Package BayesX BaylorEdPsych R Package for Baylor University Educational Psychology Quantitative Courses BB Solving and Optimizing Large-Scale Nonlinear Systems bbmle Tools for General Maximum Likelihood Estimation bcdstats A collection of functions to support B. Dudek’s APSY510/511 classes BDgraph Bayesian Structure Learning in Graphical Models using Birth-Death MCMC bdsmatrix Routines for Block Diagonal Symmetric Matrices beeswarm The Bee Swarm Plot, an Alternative to Stripchart benchmark Benchmark Experiments Toolbox BEST Bayesian Estimation Supersedes the t-Test bestglm Best Subset GLM and Regression Utilities bestNormalize Normalizing Transformation Functions betareg Beta Regression BH Boost C++ Header Files BiasedUrn Biased Urn Model Distributions bibtex Bibtex Parser biclust BiCluster Algorithms BIFIEsurvey Tools for Survey Statistics in Educational Assessment bigassertr Assertion and Message Functions biglm bounded memory linear and generalized linear models bigmemory Manage Massive Matrices with Shared Memory and Memory-Mapped Files bigmemory.sri A shared resource interface for Bigmemory Project packages bigparallelr Easy Parallel Tools bigutilsr Utility Functions for Large-scale Data bindr Parametrized Active Bindings bindrcpp An ’Rcpp’ Interface to Active Bindings binGroup Evaluation and Experimental Design for Binomial Group Testing binom Binomial Confidence Intervals For Several Parameterizations Biobase Biobase: Base functions for Bioconductor BiocGenerics S4 generic functions for Bioconductor BiocInstaller Install/Update Bioconductor, CRAN, and github Packages BiocManager Access the Bioconductor Project Package Repository BiocVersion Set the appropriate version of Bioconductor packages bit A Class for Vectors of 1-Bit Booleans 2 bit64 A S3 Class for Vectors of 64bit Integers bitops Bitwise Operations blme Bayesian Linear Mixed-Effects Models blob A Simple S3 Class for Representing Vectors of Binary Data (’BLOBS’) blockTools Block, Assign, and Diagnose Potential Interference in Randomized Experiments BMA Bayesian Model Averaging bold Interface to Bold Systems API bookdown Authoring Books and Technical Documents with R Markdown boot Bootstrap Functions (Originally by Angelo Canty for S) BootPR Bootstrap Prediction Intervals and Bias-Corrected Forecasting bootRes Bootstrapped Response and Correlation Functions bootruin A Bootstrap Test for the Probability of Ruin in the Classical Risk Process bootspecdens Testing equality of spectral densities bootStepAIC Bootstrap stepAIC bootstrap Functions for the Book "An Introduction to the Bootstrap" BradleyTerry2 Bradley-Terry Models brew Templating Framework for Report Generation brglm Bias Reduction in Binomial-Response Generalized Linear Models bridgesampling Bridge Sampling for Marginal Likelihoods and Bayes Factors brms Bayesian Regression Models using ’Stan’ Brobdingnag Very Large Numbers in R broom Convert Statistical Objects into Tidy Tibbles BRugs Interface to the ’OpenBUGS’ MCMC Software BSDA Basic Statistics and Data Analysis btergm Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood Cairo R Graphics Device using Cairo Graphics Library for Creating High-Quality Bitmap (PNG, JPEG, TIFF), Vector (PDF, SVG, PostScript) and Display (X11 and Win32) Output cairoDevice Embeddable Cairo Graphics Device Driver callr Call R from R car Companion to Applied Regression CARBayes Spatial Generalised Linear Mixed Models for Areal Unit Data CARBayesdata Data Used in the Vignettes Accompanying the CARBayes and CARBayesST Packages carData Companion to Applied Regression Data Sets caret Classification and Regression Training catdata Categorical Data caTools Tools: Moving Window Statistics, GIF, Base64, ROC AUC, etc cba Clustering for Business Analytics CDM Cognitive Diagnosis Modeling cellranger Translate Spreadsheet Cell Ranges to Rows and Columns changepoint Methods for Changepoint Detection checkmate Fast and Versatile Argument Checks chemometrics Multivariate Statistical Analysis in Chemometrics cherryblossom Cherry Blossom Run Race Results chron Chronological Objects which can Handle Dates and Times CircStats Circular Statistics, from "Topics in Circular Statistics" (2001) Ckmeans.1d.dp Optimal, Fast, and Reproducible Univariate Clustering class Functions for Classification classifly Explore classification models in high dimensions classInt Choose Univariate Class Intervals cli Helpers for Developing Command Line Interfaces cliapp Create Rich Command Line Applications clipr Read and Write from the System Clipboard clisymbols Unicode Symbols at the R Prompt cluster "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al. clv Cluster Validation Techniques 3 cmprsk Subdistribution Analysis of Competing Risks cobs Constrained B-Splines (Sparse Matrix Based) coda Output Analysis and Diagnostics for MCMC codetools Code Analysis Tools for R coin Conditional Inference Procedures in a Permutation Test Framework colorspace A Toolbox for Manipulating and Assessing Colors and Palettes colourpicker A Colour Picker Tool for Shiny and for Selecting Colours in Plots colourvalues Assigns Colours to Values combinat combinatorics utilities CommonJavaJars Useful Libraries for Building a Java Based GUI under R commonmark High Performance CommonMark and Github Markdown Rendering in R CompQuadForm Distribution Function of Quadratic Forms in Normal Variables CompRandFld Composite-Likelihood Based Analysis of Random Fields conditionz Control How Many Times Conditions are Thrown conquer Convolution-Type Smoothed Quantile Regression contfrac Continued Fractions contrast A Collection of Contrast Methods copBasic General Bivariate Copula Theory and Many Utility Functions corpcor Efficient Estimation of Covariance and (Partial) Correlation corrgram Plot a Correlogram corrplot Visualization of a Correlation Matrix covr Test Coverage for Packages cowplot Streamlined Plot Theme and Plot Annotations for ’ggplot2’ coxme Mixed Effects Cox Models cplm Compound Poisson Linear Models crayon Colored Terminal Output crossdes Construction of Crossover Designs crosstalk
Load more

Recommended publications
  • Advanced R Second Edition Chapman & Hall/CRC the R Series Series Editors John M
    Advanced R Second Edition Chapman & Hall/CRC The R Series Series Editors John M. Chambers, Department of Statistics, Stanford University, California, USA Torsten Hothorn, Division of Biostatistics, University of Zurich, Switzerland Duncan Temple Lang, Department of Statistics, University of California, Davis, USA Hadley Wickham, RStudio, Boston, Massachusetts, USA Recently Published Titles Testing R Code Richard Cotton R Primer, Second Edition Claus Thorn Ekstrøm Flexible Regression and Smoothing: Using GAMLSS in R Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, and Fernanda De Bastiani The Essentials of Data Science: Knowledge Discovery Using R Graham J. Williams blogdown: Creating Websites with R Markdown Yihui Xie, Alison Presmanes Hill, Amber Thomas Handbook of Educational Measurement and Psychometrics Using R Christopher D. Desjardins, Okan Bulut Displaying Time Series, Spatial, and Space-Time Data with R, Second Edition Oscar Perpinan Lamigueiro Reproducible Finance with R Jonathan K. Regenstein, Jr. R Markdown The Definitive Guide Yihui Xie, J.J. Allaire, Garrett Grolemund Practical R for Mass Communication and Journalism Sharon Machlis Analyzing Baseball Data with R, Second Edition Max Marchi, Jim Albert, Benjamin S. Baumer Spatio-Temporal Statistics with R Christopher K. Wikle, Andrew Zammit-Mangion, and Noel Cressie Statistical Computing with R, Second Edition Maria L. Rizzo Geocomputation with R Robin Lovelace, Jakub Nowosad, Jannes Münchow Dose-Response Analysis with R Christian Ritz, Signe M. Jensen, Daniel Gerhard, Jens C. Streibig Advanced R, Second Edition Hadley Wickham For more information about this series, please visit: https://www.crcpress.com/go/ the-r-series Advanced R Second Edition Hadley Wickham CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2019 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S.
    [Show full text]
  • 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]
  • An Evaluation of Statistical Software for Research and Instruction
    Behavior Research Methods, Instruments, & Computers 1985, 17(2),352-358 An evaluation of statistical software for research and instruction DARRELL L. BUTLER Ball State University, Muncie, Indiana and DOUGLAS B. EAMON University of Houston-Clear Lake, Houston, Texas A variety of microcomputer statistics packages were evaluated. The packages were compared on a number of dimensions, including error handling, documentation, statistical capability, and accuracy. Results indicated that there are some very good packages available both for instruc­ tion and for analyzing research data. In general, the microcomputer packages were easier to learn and to use than were mainframe packages. Furthermore, output of mainframe packages was found to be less accurate than output of some of the microcomputer packages. Many psychologists use statistical programs to analyze ware packages available on the VAX computers at Ball research data or to teach statistics, and many are interested State University: BMDP, SPSSx, SCSS, and MINITAB. in using computer software for these purposes (e.g., Versions of these programs are widely available and are Butler, 1984; Butler & Kring, 1984). The present paper used here as points ofreference to judge the strengths and provides some program descriptions, benchmarks, and weaknesses of the microcomputer packages. Although evaluations of a wide variety of marketed statistical pro­ there are many programs distributed by individuals (e.g., grams that may be useful in research and/or instruction. see Academic Computer Center of Gettysburg College, This review differs in several respects from other re­ 1984, and Eamon, 1983), none are included in the present cent reviews of statistical programs (e. g., Carpenter, review.
    [Show full text]
  • Installing R
    Installing R Russell Almond August 29, 2020 Objectives When you finish this lesson, you will be able to 1) Start and Stop R and R Studio 2) Download, install and run the tidyverse package. 3) Get help on R functions. What you need to Download R is a programming language for statistics. Generally, the way that you will work with R code is you will write scripts—small programs—that do the analysis you want to do. You will also need a development environment which will allow you to edit and run the scripts. I recommend RStudio, which is pretty easy to learn. In general, you will need three things for an analysis job: • R itself. R can be downloaded from https://cloud.r-project.org. If you use a package manager on your computer, R is likely available there. The most common package managers are homebrew on Mac OS, apt-get on Debian Linux, yum on Red hat Linux, or chocolatey on Windows. You may need to search for ‘cran’ to find the name of the right package. For Debian Linux, it is called r-base. • R Studio development environment. R Studio https://rstudio.com/products/rstudio/download/. The free version is fine for what we are doing. 1 There are other choices for development environments. I use Emacs and ESS, Emacs Speaks Statistics, but that is mostly because I’ve been using Emacs for 20 years. • A number of R packages for specific analyses. These can be downloaded from the Comprehensive R Archive Network, or CRAN. Go to https://cloud.r-project.org and click on the ‘Packages’ tab.
    [Show full text]
  • Jupyter Notebooks—A Publishing Format for Reproducible Computational Workflows
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elpub digital library Jupyter Notebooks—a publishing format for reproducible computational workflows Thomas KLUYVERa,1, Benjamin RAGAN-KELLEYb,1, Fernando PÉREZc, Brian GRANGERd, Matthias BUSSONNIERc, Jonathan FREDERICd, Kyle KELLEYe, Jessica HAMRICKc, Jason GROUTf, Sylvain CORLAYf, Paul IVANOVg, Damián h i d j AVILA , Safia ABDALLA , Carol WILLING and Jupyter Development Team a University of Southampton, UK b Simula Research Lab, Norway c University of California, Berkeley, USA d California Polytechnic State University, San Luis Obispo, USA e Rackspace f Bloomberg LP g Disqus h Continuum Analytics i Project Jupyter j Worldwide Abstract. It is increasingly necessary for researchers in all fields to write computer code, and in order to reproduce research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents. Keywords. Notebook, reproducibility, research code 1. Introduction Researchers today across all academic disciplines often need to write computer code in order to collect and process data, carry out statistical tests, run simulations or draw figures. The widely applicable libraries and tools for this are often developed as open source projects (such as NumPy, Julia, or FEniCS), but the specific code researchers write for a particular piece of work is often left unpublished, hindering reproducibility. Some authors may describe computational methods in prose, as part of a general description of research methods.
    [Show full text]
  • Statistics and GIS Assistance Help with Statistics
    Statistics and GIS assistance An arrangement for help and advice with regard to statistics and GIS is now in operation, principally for Master’s students. How do you seek advice? 1. The users, i.e. students at INA, make direct contact with the person whom they think can help and arrange a time for consultation. Remember to be well prepared! 2. Doctoral students and postdocs register the time used in Agresso (if you have questions about this contact Gunnar Jensen). Help with statistics Research scientist Even Bergseng Discipline: Forest economy, forest policies, forest models Statistical expertise: Regression analysis, models with random and fixed effects, controlled/truncated data, some time series modelling, parametric and non-parametric effectiveness analyses Software: Stata, Excel Postdoc. Ole Martin Bollandsås Discipline: Forest production, forest inventory Statistics expertise: Regression analysis, sampling Software: SAS, R Associate Professor Sjur Baardsen Discipline: Econometric analysis of markets in the forest sector Statistical expertise: General, although somewhat “rusty”, expertise in many econometric topics (all-rounder) Software: Shazam, Frontier Associate Professor Terje Gobakken Discipline: GIS og long-term predictions Statistical expertise: Regression analysis, ANOVA and PLS regression Software: SAS, R Ph.D. Student Espen Halvorsen Discipline: Forest economy, forest management planning Statistical expertise: OLS, GLS, hypothesis testing, autocorrelation, ANOVA, categorical data, GLM, ANOVA Software: (partly) Shazam, Minitab og JMP Ph.D. Student Jan Vidar Haukeland Discipline: Nature based tourism Statistical expertise: Regression and factor analysis Software: SPSS Associate Professor Olav Høibø Discipline: Wood technology Statistical expertise: Planning of experiments, regression analysis (linear and non-linear), ANOVA, random and non-random effects, categorical data, multivariate analysis Software: R, JMP, Unscrambler, some SAS Ph.D.
    [Show full text]
  • Deep Dive on Project Jupyter
    A I M 4 1 3 Deep dive on Project Jupyter Dr. Brian E. Granger Principal Technical Program Manager Co-Founder and Leader Amazon Web Services Project Jupyter © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Project Jupyter exists to develop open-source software, open standards and services for interactive and reproducible computing. https://jupyter.org/ Overview • Project Jupyter is a multi-stakeholder, open source project. • Jupyter’s flagship application is the Text Math Jupyter Notebook. • Notebook document format: • Live code, narrative text, equations, images, visualizations, audio. • ~100 programming languages supported. Live code • Over 500 contributors across >100 GitHub repositories. • Part of the NumFOCUS Foundation: • Along with NumPy, Pandas, Julia, Matplotlib,… Charts Who uses Jupyter and how? Students/Teachers Data science Data Engineers Machine learning Data Scientists Scientific computing Researchers Data cleaning and transformation Scientists Exploratory data analysis ML Engineers Analytics ML Researchers Simulation Analysts Algorithm development Reporting Data visualization Jupyter has a large and diverse user community • Many millions of Jupyter users worldwide • Thousands of AWS customers • Highly international • Over 5M public notebooks on GitHub alone Example: Dive into Deep Learning • Dive into Deep Learning is a free, open-source book about deep learning. • Zhang, Lipton, Li, Smola from AWS • All content is Jupyter Notebooks on GitHub. • 890 page PDF, hundreds of notebooks • https://d2l.ai/ © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Common threads • Jupyter serves an extremely broad range of personas, usage cases, industries, and applications • What do these have in common? Ideas of Jupyter • Computational narrative • “Real-time thinking” with a computer • Direct manipulation user interfaces that augment the writing of code “Computers are good at consuming, producing, and processing data.
    [Show full text]
  • Rkward: a Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R
    JSS Journal of Statistical Software June 2012, Volume 49, Issue 9. http://www.jstatsoft.org/ RKWard: A Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R Stefan R¨odiger Thomas Friedrichsmeier Charit´e-Universit¨atsmedizin Berlin Ruhr-University Bochum Prasenjit Kapat Meik Michalke The Ohio State University Heinrich Heine University Dusseldorf¨ Abstract R is a free open-source implementation of the S statistical computing language and programming environment. The current status of R is a command line driven interface with no advanced cross-platform graphical user interface (GUI), but it includes tools for building such. Over the past years, proprietary and non-proprietary GUI solutions have emerged, based on internal or external tool kits, with different scopes and technological concepts. For example, Rgui.exe and Rgui.app have become the de facto GUI on the Microsoft Windows and Mac OS X platforms, respectively, for most users. In this paper we discuss RKWard which aims to be both a comprehensive GUI and an integrated devel- opment environment for R. RKWard is based on the KDE software libraries. Statistical procedures and plots are implemented using an extendable plugin architecture based on ECMAScript (JavaScript), R, and XML. RKWard provides an excellent tool to manage different types of data objects; even allowing for seamless editing of certain types. The objective of RKWard is to provide a portable and extensible R interface for both basic and advanced statistical and graphical analysis, while not compromising on flexibility and modularity of the R programming environment itself. Keywords: GUI, integrated development environment, plugin, R.
    [Show full text]
  • License Agreement
    TAGARNO MOVE, FHD PRESTIGE/TREND/UNO License Agreement Version 2021.08.19 Table of Contents Table of Contents License Agreement ................................................................................................................................................ 4 Open Source & 3rd-party Licenses, MOVE ............................................................................................................ 4 Open Source & 3rd-party Licenses, PRESTIGE/TREND/UNO ................................................................................. 4 atk ...................................................................................................................................................................... 5 base-files ............................................................................................................................................................ 5 base-passwd ...................................................................................................................................................... 5 BSP (Board Support Package) ............................................................................................................................ 5 busybox.............................................................................................................................................................. 5 bzip2 .................................................................................................................................................................
    [Show full text]
  • Gröbner Basis and Structural Equation Modeling by Min Lim a Thesis
    Grobner¨ Basis and Structural Equation Modeling by Min Lim A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Statistics University of Toronto Copyright c 2010 by Min Lim Abstract Gr¨obnerBasis and Structural Equation Modeling Min Lim Doctor of Philosophy Graduate Department of Statistics University of Toronto 2010 Structural equation models are systems of simultaneous linear equations that are gener- alizations of linear regression, and have many applications in the social, behavioural and biological sciences. A serious barrier to applications is that it is easy to specify models for which the parameter vector is not identifiable from the distribution of the observable data, and it is often difficult to tell whether a model is identified or not. In this thesis, we study the most straightforward method to check for identification – solving a system of simultaneous equations. However, the calculations can easily get very complex. Gr¨obner basis is introduced to simplify the process. The main idea of checking identification is to solve a set of finitely many simultaneous equations, called identifying equations, which can be transformed into polynomials. If a unique solution is found, the model is identified. Gr¨obner basis reduces the polynomials into simpler forms making them easier to solve. Also, it allows us to investigate the model-induced constraints on the covariances, even when the model is not identified. With the explicit solution to the identifying equations, including the constraints on the covariances, we can (1) locate points in the parameter space where the model is not iden- tified, (2) find the maximum likelihood estimators, (3) study the effects of mis-specified models, (4) obtain a set of method of moments estimators, and (5) build customized parametric and distribution free tests, including inference for non-identified models.
    [Show full text]
  • R G M B.Ec. M.Sc. Ph.D
    Rolando Kristiansand, Norway www.bayesgroup.org Gonzales Martinez +47 41224721 [email protected] JOBS & AWARDS M.Sc. B.Ec. Applied Ph.D. Economics candidate Statistics Jobs Events Awards Del Valle University University of Alcalá Universitetet i Agder 2018 Bolivia, 2000-2004 Spain, 2009-2010 Norway, 2018- 1/2018 Ph.D scholarship 8/2017 International Consultant, OPHI OTHER EDUCATION First prize, International Young 12/2016 J-PAL Course on Evaluating Social Programs Macro-prudential Policies Statistician Prize, IAOS Massachusetts Institute of Technology - MIT International Monetary Fund (IMF) 5/2016 Deputy manager, BCP bank Systematic Literature Reviews and Meta-analysis Theorizing and Theory Building Campbell foundation - Vrije Universiteit Halmstad University (HH, Sweden) 1/2016 Consultant, UNFPA Bayesian Modeling, Inference and Prediction Multidimensional Poverty Analysis University of Reading OPHI, Oxford University Researcher, data analyst & SKILLS AND TECHNOLOGIES 1/2015 project coordinator, PEP Academic Research & publishing Financial & Risk analysis 12 years of experience making economic/nancial analysis 3/2014 Director, BayesGroup.org and building mathemati- Consulting cal/statistical/econometric models for the government, private organizations and non- prot NGOs Research & project Mathematical 1/2013 modeling coordinator, UNFPA LANGUAGES 04/2012 First award, BCG competition Spanish (native) Statistical analysis English (TOEFL: 106) 9/2011 Financial Economist, UDAPE MatLab Macro-economic R, RStudio 11/2010 Currently, I work mainly analyst, MEFP Stata, SPSS with MatLab and R . SQL, SAS When needed, I use 3/2010 First award, BCB competition Eviews Stata, SAS or Eviews. I started working in LaTex, WinBugs Python recently. 8/2009 M.Sc. scholarship Python, Spyder RECENT PUBLICATIONS Disjunction between universality and targeting of social policies: Demographic opportunities for depolarized de- velopment in Paraguay.
    [Show full text]
  • Centos 7 and RHEL 7 Installation Guide (X64)
    CentOS 7 and RHEL 7 Installation Guide (x64) Release Notes for Indigo 2.0.1 Better XFT and UTF-8/Unicode support Improved version of winterm, adminterm and sshterm based on the new MXTerm and dynamic font face settings (font name + size) Improvements in libXt SGI Scheme loader 5Dwm v2.3 (multi-screen support with Xinerama) Recompiled all libraries and apps to support better application binary interface (ABI) across Linux distributions Revised Shutdown and Restart Admin-Screens (from toolchest) New configuration files in $HOME/.maxxdesktop to support Fonts, themes and other customization Command line tools to switch between CLASSIC and MODERN look and feel Custom XSettings Daemon and MaXX specific settings Support Dynamic GTK+ SGI Theme change New version of Nedit (called XNedit) with full unicode support and antialiased text rendering Toolchest dark SGI Scheme support for main-menu icon Introducing an 'user-based' X11 Resources settings and customization manager. Unified look and feel for both gmemusage and the new gr_osview2 New version of gr_osview (based on xosview 2) Entire new 32 bit sub-systems and libraries to support 32 bit apps under MaXX Interactive Desktop 64 bit (separate download) Some new/old SGI demos Many bug fixes and improvements Small fixes to improve compatibility and usability for: CentOS, Fedora and Ubuntu Prerequisites These are below the prerequisites for installing and running MaXX and some older X11/Motif applications... Storage Space Requirements 75MB in /tmp partition (something it is part of the / 'root' partition)
    [Show full text]