ROMI-3.1 Least-Cost Lumber Grade Mix Solver Using Open Source Statistical Software

2010 ◽  
Vol 60 (5) ◽  
pp. 432-439 ◽  
Author(s):  
Rebecca A. Buck ◽  
Urs Buehlmann ◽  
R. Edward Thomas
Keyword(s):  
Open Source ◽  
Statistical Software ◽  
Lumber Grade

scholarly journals Introducing the StataStan Interface for Fast, Complex Bayesian Modeling Using Stan

2017 ◽  
Vol 17 (2) ◽  
pp. 330-342 ◽  
Author(s):  
Robert L. Grant ◽  
Bob Carpenter ◽  
Daniel C. Furr ◽  
Andrew Gelman
Keyword(s):  
Bayesian Inference ◽  
Item Response Theory ◽  
Open Source ◽  
Item Response ◽  
Bayesian Modeling ◽  
Inference Engine ◽  
Software Components ◽  
Statistical Software ◽  
Simulation Based ◽  
Item Response Theory Models

In this article, we present StataStan, an interface that allows simulation-based Bayesian inference in Stata via calls to Stan, the flexible, open-source Bayesian inference engine. Stan is written in C++, and Stata users can use the commands stan and windowsmonitor to run Stan programs from within Stata. We provide a brief overview of Bayesian algorithms, details of the commands available from Statistical Software Components, considerations for users who are new to Stan, and a simple example. Stan uses a different algorithm than bayesmh, BUGS, JAGS, SAS, and MLwiN. This algorithm provides considerable improvements in efficiency and speed. In a companion article, we give an extended comparison of StataStan and bayesmh in the context of item response theory models.

scholarly journals Introducción del Software Estadístico R en la Maestría en Administración de Negocios de una Universidad Mexicana / Introduction of the Statistical Software Open Source R in the Master of Business Administration Program of a Mexican University

2021 ◽  
Vol 3 (2) ◽  
pp. 1678-1692
Author(s):  
Carlos Rodríguez ◽  
Nora A. Arriaga
Keyword(s):  
Open Source ◽  
Business Administration ◽  
Master Of Business Administration ◽  
Statistical Software ◽  
Nuevas Tecnologías

En el Programa de Posgrado en Ciencias de la Administración de la UNAM se lleva a cabo un estudio educativo con la finalidad de encontrar la manera más viable para introducir el software estadístico Open Source R en la asignatura de Métodos Cuantitativos Aplicados a la Administración, de la Maestría en Administración (MBA). Con este fin se llevó a cabo una investigación descriptiva, específicamente una investigación comparativa, como sigue: durante los seis semestres comprendidos entre el semestre 2014-1 y el semestre 2016-2 se impartió la materia de manera alternada: en un semestre se siguió el programa de la asignatura y en otro se destinaron las primeras sesiones a exponer a los alumnos a las nuevas tecnologías relacionadas a la Ciencia de Datos y las ventajas del aprendizaje de R como herramienta para explotar la gran cantidad de datos que actualmente se generan. Al sustentarse esta investigación en el paradigma del positivismo, se utilizó una escala actitudinal con la intención de medir el comportamiento de los individuos, de esta manera, en cada semestre, en la tercera o cuarta sesión, se suministró un cuestionario basado en el Motivated Strategies for Learning Questionnaire (MSLQ). Los datos recopilados permiten probar la hipótesis de trabajo motivo de este estudio. Se realizó la prueba estadística no paramétrica Χ2 con la cual se pudo confirmar que es mejor una temprana exposición de las ventajas de este software en la aceptación del mismo. Este hallazgo es de utilidad para los profesores interesados en introducir R en programas de MBA.

scholarly journals Agricolae - Ten years of an open source statistical tool for experiments in breeding, agriculture and biology

2015 ◽  
Author(s):  
Felipe De Mendiburu ◽  
Reinhard Simon
Keyword(s):  
Cancer Research ◽  
Open Source ◽  
Conservation Biology ◽  
R Package ◽  
User Survey ◽  
Citation Data ◽  
Statistical Tools ◽  
Statistical Tool ◽  
Statistical Software ◽  
First Time

Plant breeders and educators working with the International Potato Center (CIP) needed freely available statistical tools. In response, we created first a set of scripts for specific tasks using the open source statistical software R. Based on this we eventually compiled the R package agricolae as it covered a niche. Here we describe for the first time its main functions in the form of an article. We also review its reception using download statistics, citation data, and feedback from a user survey. We highlight usage in our extended network of collaborators. The package has found applications beyond agriculture in fields like aquaculture, ecology, biodiversity, conservation biology and cancer research. In summary, the package agricolae is a well established statistical toolbox based on R with a broad range of applications in design and analyses of experiments also in the wider biological community .

scholarly journals A Survey of Open Source Statistical Software (OSSS) and Their Data Processing Functionalities

2021 ◽  
Vol 12 (1) ◽  
pp. 1-20
Author(s):  
Gao Niu ◽  
Richard S. Segall ◽  
Zichen Zhao ◽  
Zhijian Wu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Statistical Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Free Software ◽  
Statistical Software ◽  
Artificial Intelligence Application ◽  
Intelligence Application

This paper discusses the definitions of open source software, free software and freeware, and the concept of big data. The authors then introduce R and Python as the two most popular open source statistical software (OSSS). Additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, are also introduced in this paper with function descriptions and modeling examples. They further discuss OSSS's capability in artificial intelligence application and modeling and Popular OSSS-based machine learning libraries and systems. The paper intends to provide a reference for readers to make proper selections of open source software when statistical analysis tasks are needed. In addition, working platform and selective numerical, descriptive and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.

scholarly journals The JASP Guidelines for Conducting and Reporting a Bayesian Analysis

2019 ◽  
Author(s):  
Johnny van Doorn ◽  
Don van den Bergh ◽  
Udo Bohm ◽  
Fabian Dablander ◽  
Koen Derks ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Bayesian Analysis ◽  
Open Source ◽  
Empirical Research ◽  
Statistical Reasoning ◽  
Research Setting ◽  
Statistical Software ◽  
Bayesian Procedures ◽  
Practical Guidelines

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared toward analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.

Open Source Software (OSS) for Big Data

2021 ◽  
pp. 858-875
Author(s):  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Open Source Software ◽  
Statistical Software ◽  
Big Data Processing ◽  
Open Source Data ◽  
Source Data ◽  
And Storage ◽  
Aggregation Analysis

This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open source data tools and frameworks such as SMACK that is acronym for open source technologies Spark, Mesos, Akka, Cassandra, and Kafka that together compose the ingestion, aggregation, analysis, and storage layers for Big Data processing. Tabular summaries and categories for 38 Open Source Statistical Software (OSSS) are provided that include for each listing of features and URLs for free downloads. The current challenges of Big Data and Open Source Software are also discussed.

Introduction to the Popular Open Source Statistical Software (OSSS)

2020 ◽  
pp. 73-110
Author(s):  
Zhijian Wu ◽  
Zichen Zhao ◽  
Gao Niu
Keyword(s):  
Statistical Analysis ◽  
User Interface ◽  
Open Source ◽  
Graphical User Interface ◽  
Proper Selection ◽  
Development Environment ◽  
Integrated Development ◽  
Statistical Software ◽  
Analysis Task ◽  
Selection Of

This chapter first introduces the two most popular Open Source Statistical Software (OSSS), R and Python, along with their Integrated Development Environment (IDE) and Graphical User Interface (GUI). Secondly, additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, will also be introduced in this chapter with function descriptions and modeling examples. The chapter intends to create a reference for readers to make proper selection of the Open Source Software when a statistical analysis task is in demand. The chapter describes software explicitly in words. In addition, working platform and selective numerical, descriptive, and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.

Open Source Software (OSS) for Big Data

2020 ◽  
pp. 50-72
Author(s):  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Open Source Software ◽  
Statistical Software ◽  
Big Data Processing ◽  
Open Source Data ◽  
Source Data ◽  
And Storage ◽  
Aggregation Analysis

This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open source data tools and frameworks such as SMACK that is acronym for open source technologies Spark, Mesos, Akka, Cassandra, and Kafka that together compose the ingestion, aggregation, analysis, and storage layers for Big Data processing. Tabular summaries and categories for 38 Open Source Statistical Software (OSSS) are provided that include for each listing of features and URLs for free downloads. The current challenges of Big Data and Open Source Software are also discussed.

統計教育ツールとしての jamovi / Introducing jamovi as a tool for education in statistics.

2019 ◽  
Author(s):  
Yoshimasa Majima ◽  
Akiyuki Nagai ◽  
Satoru Ishikawa ◽  
Akiko Fujiki ◽  
Toshio Matsura
Keyword(s):  
Open Source ◽  
Programming Language ◽  
Statistical Testing ◽  
Educational Tool ◽  
Statistical Software ◽  
Potential Benefits ◽  
Statistical Programming ◽  
User Friendly ◽  
Successful Candidate

オープンソースの統計ソフトウェアである jamovi が有償ソフトウェアに比べて，統計教育のためのツールとして何がどのように優れているかを論じ，jamovi による統計分析の例を解説した。/ We proposed that open-source statistical software “jamovi” is a successful candidate of educational tool in statistics, because it has a user-friendly GUI and is based on powerful statistical programming language R. In this paper, we argued the potential benefits of “jamovi” and introduced how it works with typical statistical testing.

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