The R Project for Statistical Computing a Free Software Environment For

Total Page:16

File Type:pdf, Size:1020Kb

The R Project for Statistical Computing a Free Software Environment For The R Project for Statistical Computing A free software environment for statistical computing and graphics that runs on a wide variety of UNIX platforms, Windows and MacOS OpenStat OpenStat is a general-purpose statistics package that you can download and install for free. It was originally written as an aid in the teaching of statistics to students enrolled in a social science program. It has been expanded to provide procedures useful in a wide variety of disciplines. It has a similar interface to SPSS SOFA A basic, user-friendly, open-source statistics, analysis, and reporting package PSPP PSPP is a program for statistical analysis of sampled data. It is a free replacement for the proprietary program SPSS, and appears very similar to it with a few exceptions TANAGRA A free, open-source, easy to use data-mining package PAST PAST is a package created with the palaeontologist in mind but has been adopted by users in other disciplines. It’s easy to use and includes a large selection of common statistical, plotting and modelling functions AnSWR AnSWR is a software system for coordinating and conducting large-scale, team-based analysis projects that integrate qualitative and quantitative techniques MIX An Excel-based tool for meta-analysis Free Statistical Software This page links to free software packages that you can download and install on your computer from StatPages.org Free Statistical Software This page links to free software packages that you can download and install on your computer from freestatistics.info Free Software Information and links from the Resources for Methods in Evaluation and Social Research site You can sort the table below by clicking on the column names. Software Name License Data Mining Currently Updated Type Approaches Available? On ADAPA Enterprise Commercial Classification Yes 17 Apr Edition Licence Discovery, Cluster 2011 - ( ) Discovery, Regression 18:17 Discovery, Data Visualisation ADAPA Predictive Commercial Classification Yes 07 Mar Analytics Licence Discovery, Cluster 2009 - ( ) Discovery, Regression 06:36 Discovery, Association Discovery Angoss Commercial Classification Yes 11 Apr KnowledgeSEEKER Licence Discovery, Discovery 2011 - ( ) Visualisation 18:04 ANGOSS Commercial Classification Yes 17 Apr KnowledgeSTUDIO Licence Discovery, Cluster 2011 - ( ) Discovery, Data 18:41 Visualisation, Discovery Visualisation GeneXproTools 4.0 Commercial Classification Yes 31 Jan ( ) Licence Discovery, Regression 2012 - Discovery, Data 13:24 Visualisation, Discovery Visualisation, Sequence Analysis ARMiner Free - Open Association Discovery Yes 17 Apr ( ) Source 2011 - Licence 18:19 ARTool Free - Open Association Discovery Yes 13 Mar ( ) Source 2008 - Licence 15:27 Analysis Studio Commercial Classification Yes 01 Feb Discovery, Regression 2010 - Software Name License Data Mining Currently Updated Type Approaches Available? On ( ) Licence Discovery, Outlier 08:34 Discovery Angoss Commercial Cluster Discovery, Yes 20 Oct StrategyBUILDER Licence Regression Discovery, 2008 - ( ) Data Visualisation, 19:09 Discovery Visualisation BLIASoft Knowledge Commercial Classification Yes 29 Feb Discovery Licence Discovery, Regression 2008 - ( ) Discovery, Association 18:53 Discovery, Data Visualisation Bayes Server Commercial Classification Yes 30 Dec ( ) Licence Discovery, Cluster 2010 - Discovery, Regression 16:21 Discovery, Outlier Discovery, Discovery Visualisation, Sequence Analysis CART® Commercial Classification Yes 07 Apr (Classification and Licence Discovery 2011 - Regression Trees) 16:31 ( ) Compumine Rule Free - Other Classification Yes 13 Mar Discovery System Licence Discovery, Regression 2008 - ( ) Discovery, Data 19:25 Visualisation, Discovery Visualisation Mil Shield Commercial Yes 04 Nov ( ) Licence 2010 - 06:59 DMax Chemistry Commercial Yes 17 Nov Assistant Licence 2006 - ( ) 11:06 Data Applied Commercial Classification Yes 20 Aug ( ) Licence Discovery, Cluster 2011 - Discovery, Association Software Name License Data Mining Currently Updated Type Approaches Available? On Discovery, Outlier 09:45 Discovery, Data Visualisation, Discovery Visualisation, Web Analytics DataDetective Commercial Classification Yes 27 Jul ( ) Licence Discovery, Cluster 2009 - Discovery, Association 19:10 Discovery, Text Mining, Outlier Discovery, Data Visualisation, Discovery Visualisation Eaagle Full Text Commercial Classification Yes 27 Jul Mapper (FTM) Licence Discovery, Cluster 2009 - ( ) Discovery, Regression 11:02 Discovery, Association Discovery, Text Mining, Outlier Discovery, Data Visualisation, Discovery Visualisation, Sequence Analysis, Web Analytics, Social Network Analysis Viscovery Commercial Classification Yes 08 Apr ( ) Licence Discovery, Cluster 2008 - Discovery, Regression 15:08 Discovery, Text Mining, Outlier Discovery, Data Visualisation, Discovery Visualisation, Web Analytics 1st-Profit Commercial Yes 09 Sep ( ) Licence 2010 - 14:52 Software Name License Data Mining Currently Updated Type Approaches Available? On GMDH Shell Commercial Classification Yes 23 Jun ( ) Licence Discovery, Regression 2014 - Discovery, Data 22:17 Visualisation, Discovery Visualisation, Sequence Analysis Gait-CAD Free - Open Classification Yes 25 Jan ( ) Source Discovery, Cluster 2010 - Licence Discovery, Regression 14:01 Discovery, Data Visualisation GenIQ Commercial Regression Discovery Yes 17 Apr ( ) Licence 2011 - 18:20 GhostMiner Commercial Classification Yes 17 Apr ( ) Licence Discovery, Cluster 2011 - Discovery, Outlier 18:21 Discovery, Data Visualisation Helium Scraper Commercial Text Mining, Web Yes 07 Apr ( ) Licence Analytics 2011 - 23:56 KXEN Analytic Commercial Classification Yes 29 Dec Framework Licence Discovery, Cluster 2008 - ( ) Discovery, Regression 22:06 Discovery, Association Discovery, Text Mining, Outlier Discovery, Data Visualisation, Sequence Analysis, Web Analytics, Social Network Analysis KEEL (Knowledge Free - Other Classification Yes 13 Mar Extraction based on Licence Discovery, Cluster 2008 - Evolutionary Discovery, Regression 19:00 Learning) Discovery, Association ( ) Discovery, Data Software Name License Data Mining Currently Updated Type Approaches Available? On Visualisation, Discovery Visualisation Angoss Commercial Text Mining, Data Yes 27 Jul KnowledgeSEEKER Licence Visualisation 2009 - for salesforce.com 16:36 ( ) MARS® (Multivariate Commercial Regression Discovery, Yes 07 Apr Adaptive Regression Licence Outlier Discovery 2011 - Splines) 16:31 ( ) MDR Free - Open Classification Yes 17 Apr ( ) Source Discovery, Association 2011 - Licence Discovery 18:23 Magnum Opus Demo Free - Other Association Discovery Yes 27 Jul ( ) Licence 2009 - 11:22 Magnum Opus Commercial Association Discovery Yes 17 Jan ( ) Licence 2010 - 04:40 Mil Firewall Commercial Social Network Yes 22 May ( ) Licence Analysis 2009 - 05:44 Mil Shield Commercial Classification Yes 03 May ( ) Licence Discovery 2010 - 10:45 11Ants Model Builder Commercial Classification Yes 25 Aug ( ) Licence Discovery, Regression 2010 - Discovery, Outlier 22:35 Discovery Molegro Data Commercial Cluster Discovery, Yes 17 Jun Modeller Licence Regression Discovery, 2008 - ( ) Outlier Discovery, 09:33 Data Visualisation, Discovery Visualisation Software Name License Data Mining Currently Updated Type Approaches Available? On Mozenda Unknown Yes 18 Aug ( ) Licence 2009 - 21:44 NovoSpark Visualizer Commercial Classification Yes 29 Feb ( ) Licence Discovery, Cluster 2008 - Discovery, Data 17:51 Visualisation, Discovery Visualisation, Sequence Analysis OmniAnalyser Commercial Cluster Discovery, Yes 23 Nov ( ) Licence Association Discovery, 2011 - Text Mining, Data 14:07 Visualisation, Discovery Visualisation, Web Analytics, Social Network Analysis perSimplex Commercial Cluster Discovery, Yes 28 Oct ( ) Licence Data Visualisation, 2009 - Discovery 09:05 Visualisation Plug&Score Modeler Commercial Classification Yes 03 Mar ( ) Licence Discovery, Regression 2011 - Discovery, Data 11:03 Visualisation PolyAnalyst Commercial Classification Yes 18 Mar ( ) Licence Discovery, Cluster 2008 - Discovery, Regression 23:08 Discovery, Text Mining, Outlier Discovery, Data Visualisation, Social Network Analysis R Free - Open Classification Yes 17 Apr ( ) Source Discovery, Cluster 2011 - Licence Discovery, Regression 18:26 Discovery, Association Discovery, Text Software Name License Data Mining Currently Updated Type Approaches Available? On Mining, Outlier Discovery, Data Visualisation, Discovery Visualisation, Sequence Analysis, Web Analytics, Social Network Analysis RandomForests® Commercial Yes 07 Apr ( ) Licence 2011 - 16:30 RapAnalyst Commercial Data Visualisation, Yes 02 Mar ( ) Licence Discovery 2006 - Visualisation, 15:45 Classification Discovery, Cluster Discovery, Association Discovery, Outlier Discovery RapidMiner Free - Open Classification Yes 13 Mar ( ) Source Discovery, Cluster 2008 - Licence Discovery, Regression 15:18 Discovery, Association Discovery, Text Mining, Outlier Discovery, Data Visualisation SAS Enterprise Miner Commercial Classification Yes 27 Jul ( ) Licence Discovery, Cluster 2009 - Discovery, Regression 19:09 Discovery, Association Discovery, Outlier Discovery, Data Visualisation, Web Analytics Senn Unknown Unknown 20 Aug Licence 2011 - 09:43 SPAD Commercial Classification Yes 17 Apr Software Name License Data Mining Currently Updated Type Approaches Available? On ( ) Licence Discovery, Cluster 2011 - Discovery, Regression 18:26 Discovery, Association Discovery, Text Mining, Outlier Discovery, Data Visualisation, Discovery Visualisation, Social
Recommended publications
  • Seminar to Advanced Macroeconomics
    Seminar to Advanced Macroeconomics Jaromír Baxa IES FSV UK, WS 2010/2011 Introduction Aim of the seminar: Overview over empirical methods used in macro to make your horizons wider. Easy applications of econometrics to macroeconomic topics discussed in the lectures Using econometric software Talking about your Project Tasks and discussion about Problem Sets Why Empirical Seminar? The Role of Empirical Work in Macro Correspondence between the theory and real data Forecasting and economic policy Finding empirical evidence to build new theories Fundamental ucertainty in econometrics: choice of variables => Robustness over different datasets, over different additional variables... => We should always keep in mind this uncertainty and ask: Are my results good because of the datasets? Methods Descriptive statistics, tests... Some nonparametric methods: tests, density estimates Linear Regression Panel data regression Principal Components method Time series: seasonal adjustment, trends... Dynamic models (very brief introduction) ... Software You can't do empirical work without it. There are many software packages for econometrics: Commercial: TSP, SAS, Stata, E-views, PC-Give, Gauss, S-Plus and many others Freeware/Open Source/Shareware without limitations: Gretl, R-Project, Ox See http://freestatistics.altervista.org/stat.php for comprehensive list. Use whatever you want to And bring your laptop with (if you can) Gretl Available in Room 016: TSP (GiweWin GUI), SPSS for Windows 10.0, R (with necessary libraries), Gretl, JMulti Gretl: http://gretl.sourceforge.net, GNU GPL licence, crossplatform. Have a look into documentation: manual as an textbook available. Don't forget to install seasonal adjustment methods, we will use them in a couple of weeks.
    [Show full text]
  • A Comparison of Six Numerical Software Packages for Educational Use in the Chemical Engineering Curriculum
    SESSION 2520 A COMPARISON OF SIX NUMERICAL SOFTWARE PACKAGES FOR EDUCATIONAL USE IN THE CHEMICAL ENGINEERING CURRICULUM Mordechai Shacham Department of Chemical Engineering Ben-Gurion University of the Negev P. O. Box 653 Beer Sheva 84105, Israel Tel: (972) 7-6461481 Fax: (972) 7-6472916 E-mail: [email protected] Michael B. Cutlip Department of Chemical Engineering University of Connecticut Box U-222 Storrs, CT 06269-3222 Tel: (860)486-0321 Fax: (860)486-2959 E-mail: [email protected] INTRODUCTION Until the early 1980’s, computer use in Chemical Engineering Education involved mainly FORTRAN and less frequently CSMP programming. A typical com- puter assignment in that era would require the student to carry out the following tasks: 1.) Derive the model equations for the problem at hand, 2.) Find an appropri- ate numerical method to solve the model (mostly NLE’s or ODE’s), 3.) Write and debug a FORTRAN program to solve the problem using the selected numerical algo- rithm, and 4.) Analyze the results for validity and precision. It was soon recognized that the second and third tasks of the solution were minor contributions to the learning of the subject material in most chemical engi- neering courses, but they were actually the most time consuming and frustrating parts of computer assignments. The computer indeed enabled the students to solve realistic problems, but the time spent on technical details which were of minor rele- vance to the subject matter was much too long. In order to solve this difficulty, there was a tendency to provide the students with computer programs that could solve one particular type of a problem.
    [Show full text]
  • Rapid Research with Computer Algebra Systems
    doi: 10.21495/71-0-109 25th International Conference ENGINEERING MECHANICS 2019 Svratka, Czech Republic, 13 – 16 May 2019 RAPID RESEARCH WITH COMPUTER ALGEBRA SYSTEMS C. Fischer* Abstract: Computer algebra systems (CAS) are gaining popularity not only among young students and schol- ars but also as a tool for serious work. These highly complicated software systems, which used to just be regarded as toys for computer enthusiasts, have reached maturity. Nowadays such systems are available on a variety of computer platforms, starting from freely-available on-line services up to complex and expensive software packages. The aim of this review paper is to show some selected capabilities of CAS and point out some problems with their usage from the point of view of 25 years of experience. Keywords: Computer algebra system, Methodology, Wolfram Mathematica 1. Introduction The Wikipedia page (Wikipedia contributors, 2019a) defines CAS as a package comprising a set of algo- rithms for performing symbolic manipulations on algebraic objects, a language to implement them, and an environment in which to use the language. There are 35 different systems listed on the page, four of them discontinued. The oldest one, Reduce, was publicly released in 1968 (Hearn, 2005) and is still available as an open-source project. Maple (2019a) is among the most popular CAS. It was first publicly released in 1984 (Maple, 2019b) and is still popular, also among users in the Czech Republic. PTC Mathcad (2019) was published in 1986 in DOS as an engineering calculation solution, and gained popularity for its ability to work with typeset mathematical notation in combination with automatic computations.
    [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]
  • 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]
  • Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology Polina Lemenkova
    Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology Polina Lemenkova To cite this version: Polina Lemenkova. Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology. International Journal of Environmental Trends (IJENT), 2019, 3 (1), pp.39 - 59. hal-02163671 HAL Id: hal-02163671 https://hal.archives-ouvertes.fr/hal-02163671 Submitted on 3 Jul 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License International Journal of Environmental Trends (IJENT) 2019: 3 (1),39-59 ISSN: 2602-4160 Research Article REGRESSION MODELS BY GRETL AND R STATISTICAL PACKAGES FOR DATA ANALYSIS IN MARINE GEOLOGY Polina Lemenkova 1* 1 ORCID ID number: 0000-0002-5759-1089. Ocean University of China, College of Marine Geo-sciences. 238 Songling Rd., 266100, Qingdao, Shandong, P. R. C. Tel.: +86-1768-554-1605. Abstract Received 3 May 2018 Gretl and R statistical libraries enables to perform data analysis using various algorithms, modules and functions. The case study of this research consists in geospatial analysis of Accepted the Mariana Trench, a hadal trench located in the Pacific Ocean.
    [Show full text]
  • Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
    UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done.
    [Show full text]
  • An Introduction to the SAS System
    An Introduction to the SAS System Dileep K. Panda Directorate of Water Management Bhubaneswar-751023 [email protected] Introduction The SAS – Statistical Analysis System (erstwhile expansion of SAS) - is the one of the most widely used Statistical Software package by the academic circles and Industry. The SAS software was developed in late 1960s at North Carolina State University and in 1976 SAS Institute was formed. The SAS system is a collection of products, available from the SAS Institute in North Carolina. SAS software is a combination of a statistical package, a data – base management system, and a high level programming language. The SAS is an integrated system of software solutions that performs the following tasks: Data entry, retrieval, and management Report writing and graphics design Statistical and mathematical analysis Business forecasting and decision support Operations research and project management Applications development At the core of the SAS System is the Base SAS software. The Base SAS software includes a fourth-generation programming language and ready-to-use programs called procedures. These integrated procedures handle data manipulation, information storage and retrieval, statistical analysis, and report writing. Additional components offer capabilities for data entry, retrieval, and management; report writing and graphics; statistical and mathematical analysis; business planning, forecasting, and decision support; operations research and project management; quality improvement; and applications development. In general, the Base SAS software has the following capabilities A data management facility A programming language Data analysis and reporting utilities Learning to use Base SAS enables you to work with these features of SAS. It also prepares you to learn other SAS products, because all SAS products follow the same basic rules.
    [Show full text]
  • 8 the Software Jmulti
    P1: IML CB698-Driver CB698-LUTKEPOHI CB698-LUTKEPOHI-Sample.cls August 25, 2006 17:44 8 The Software JMulTi Markus Kr¨atzig 8.1 Introduction to JMulTi 8.1.1 Software Concept This chapter gives a general overview of the software by which the examples in this book can be reproduced; it is freely available via the Internet.1 The information given here covers general issues and concepts of JMulTi. Detailed descriptions on how to use certain methods in the program are left to the help system installed with the software. JMulTi is an interactive JAVA application designed for the specific needs of time series analysis. It does not compute the results of the statistical calcu- lations itself but delegates this part to a computational engine via a communi- cations layer. The range of its own computing functions is limited and is only meant to support data transformations to provide input for the various statistical routines. Like other software packages, JMulTi contains graphical user interface (GUI) components that simplify tasks common to empirical analysis – espe- cially reading in data, transforming variables, creating new variables, editing data, and saving data sets. Most of its functions are accessible by simple mouse interaction. Originally the software was designed as an easy-to-use GUI for complex and difficult-to-use econometric procedures written in GAUSS that were not available in other packages. Because this concept has proved to be quite fruit- ful, JMulTi has now evolved into a comprehensive modeling environment for multiple time series analysis. The underlying general functionality has been bundled in the software framework JStatCom, which is designed as a ready-made platform for the creation of various statistical applications by developers.
    [Show full text]
  • Why SAS Programmers Should Learn Python Too Michael Stackhouse, Covance, Inc
    PharmaSUG 2018 - Paper AD-12 Why SAS Programmers Should Learn Python Too Michael Stackhouse, Covance, Inc. ABSTRACT Day to day work can often require simple, yet repetitive tasks. All companies have tedious processes that may involve moving and renaming files, making redundant edits to code, and more. Often, one might also want to find or summarize information scattered amongst many different files as well. While SAS programmers are very familiar with the power of SAS, the power of some lesser known but readily available tools may go unnoticed. The programming language Python has many capabilities that can easily automate and facilitate these tasks. Additionally, many companies have adopted Linux and UNIX as their OS of choice. With a small amount of background, and an understanding of Linux/UNIX command line syntax, powerful tools can be developed to eliminate these tedious activities. Better yet, these tools can be written to “talk back” to the user, making them more user friendly for those averse to venturing from their SAS editor. This paper will explore the benefits of how Python can be adopted into a SAS programming world. INTRODUCTION SAS is a great language, and it does what it’s designed to do very well, but other languages are better catered to different tasks. One language in particular that can serve a number of different purposes is Python. This is for a number of reasons. First off, it’s easy. Python has a very spoken-word style of syntax that is both intuitive to use and easy to learn. This makes it a very attractive language for newcomers, but also an attractive language to someone like a SAS programmer.
    [Show full text]
  • Kwame Nkrumah University of Science and Technology, Kumasi
    KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, KUMASI, GHANA Assessing the Social Impacts of Illegal Gold Mining Activities at Dunkwa-On-Offin by Judith Selassie Garr (B.A, Social Science) A Thesis submitted to the Department of Building Technology, College of Art and Built Environment in partial fulfilment of the requirement for a degree of MASTER OF SCIENCE NOVEMBER, 2018 DECLARATION I hereby declare that this work is the result of my own original research and this thesis has neither in whole nor in part been prescribed by another degree elsewhere. References to other people’s work have been duly cited. STUDENT: JUDITH S. GARR (PG1150417) Signature: ........................................................... Date: .................................................................. Certified by SUPERVISOR: PROF. EDWARD BADU Signature: ........................................................... Date: ................................................................... Certified by THE HEAD OF DEPARTMENT: PROF. B. K. BAIDEN Signature: ........................................................... Date: ................................................................... i ABSTRACT Mining activities are undertaken in many parts of the world where mineral deposits are found. In developing nations such as Ghana, the activity is done both legally and illegally, often with very little or no supervision, hence much damage is done to the water bodies where the activities are carried out. This study sought to assess the social impacts of illegal gold mining activities at Dunkwa-On-Offin, the capital town of Upper Denkyira East Municipality in the Central Region of Ghana. The main objectives of the research are to identify factors that trigger illegal mining; to identify social effects of illegal gold mining activities on inhabitants of Dunkwa-on-Offin; and to suggest effective ways in curbing illegal mining activities. Based on the approach to data collection, this study adopts both the quantitative and qualitative approach.
    [Show full text]
  • SQSTM1 Mutations in Familial and Sporadic Amyotrophic Lateral Sclerosis
    ORIGINAL CONTRIBUTION SQSTM1 Mutations in Familial and Sporadic Amyotrophic Lateral Sclerosis Faisal Fecto, MD; Jianhua Yan, MD, PhD; S. Pavan Vemula; Erdong Liu, MD; Yi Yang, MS; Wenjie Chen, MD; Jian Guo Zheng, MD; Yong Shi, MD, PhD; Nailah Siddique, RN, MSN; Hasan Arrat, MD; Sandra Donkervoort, MS; Senda Ajroud-Driss, MD; Robert L. Sufit, MD; Scott L. Heller, MD; Han-Xiang Deng, MD, PhD; Teepu Siddique, MD Background: The SQSTM1 gene encodes p62, a major In silico analysis of variants was performed to predict al- pathologic protein involved in neurodegeneration. terations in p62 structure and function. Objective: To examine whether SQSTM1 mutations con- Results: We identified 10 novel SQSTM1 mutations (9 tribute to familial and sporadic amyotrophic lateral scle- heterozygous missense and 1 deletion) in 15 patients (6 rosis (ALS). with familial ALS and 9 with sporadic ALS). Predictive in silico analysis classified 8 of 9 missense variants as Design: Case-control study. pathogenic. Setting: Academic research. Conclusions: Using candidate gene identification based on prior biological knowledge and the functional pre- Patients: A cohort of 546 patients with familial diction of rare variants, we identified several novel (n=340) or sporadic (n=206) ALS seen at a major aca- SQSTM1 mutations in patients with ALS. Our findings demic referral center were screened for SQSTM1 muta- provide evidence of a direct genetic role for p62 in ALS tions. pathogenesis and suggest that regulation of protein deg- radation pathways may represent an important thera- Main Outcome Measures: We evaluated the distri- peutic target in motor neuron degeneration. bution of missense, deletion, silent, and intronic vari- ants in SQSTM1 among our cohort of patients with ALS.
    [Show full text]