R Journal最新文献_第7页

SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data. 一个半竞争风险:一个独立的和聚类相关的半竞争风险数据分析的R包。

IF 2.1 4区计算机科学

R Journal Pub Date : 2019-06-01 Epub Date: 2019-08-20 DOI: 10.32614/rj-2019-038

Danilo Alvares, Sebastien Haneuse, Catherine Lee, Kyu Ha Lee

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引用次数: 15

What's for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R. 什么是dynr:一个在R中的线性和非线性动态建模包。

IF 2.1 4区计算机科学

R Journal Pub Date : 2019-06-01 DOI: 10.32614/rj-2019-012

Lu Ou, Michael D Hunter, Sy-Miin Chow

引用次数: 38

rFSA: An R Package for Finding Best Subsets and Interactions. rFSA:一个寻找最佳子集和交互的R包。

IF 2.1 4区计算机科学

R Journal Pub Date : 2018-12-01 Epub Date: 2018-12-08 DOI: 10.32614/rj-2018-059

Joshua Lambert, Liyu Gong, Corrine F Elliott, Katherine Thompson, Arnold Stromberg

引用次数: 24

Semiparametric Generalized Linear Models with the gldrm Package. 带有gldrm软件包的半参数广义线性模型。

IF 2.1 4区计算机科学

R Journal Pub Date : 2018-07-01

Michael J Wurm, Paul J Rathouz

引用次数: 0

MGLM: An R Package for Multivariate Categorical Data Analysis. 多变量分类数据分析的R包。

IF 2.1 4区计算机科学

R Journal Pub Date : 2018-07-01 DOI: 10.32614/rj-2018-015

Juhyun Kim, Yiwen Zhang, Joshua Day, Hua Zhou

引用次数: 14

Semiparametric Generalized Linear Models with the gldrm Package 具有gldrm包的半参数广义线性模型

IF 2.1 4区计算机科学

R Journal Pub Date : 2018-07-01 DOI: 10.32614/RJ-2018-027

Mike Wurm, P. Rathouz

引用次数: 3

A System for an Accountable Data Analysis Process in R. 一个负责任的数据分析过程系统。

IF 2.1 4区计算机科学

R Journal Pub Date : 2018-07-01 Epub Date: 2018-05-15

Jonathan Gelfond, Martin Goros, Brian Hernandez, Alex Bokov

{"title":"A System for an Accountable Data Analysis Process in R.","authors":"Jonathan Gelfond, Martin Goros, Brian Hernandez, Alex Bokov","doi":"","DOIUrl":"","url":null,"abstract":"Efficiently producing transparent analyses may be difficult for beginners or tedious for the experienced. This implies a need for computing systems and environments that can efficiently satisfy reproducibility and accountability standards. To this end, we have developed a system, R package, and R Shiny application called adapr (Accountable Data Analysis Process in R) that is built on the principle of accountable units. An accountable unit is a data file (statistic, table or graphic) that can be associated with a provenance, meaning how it was created, when it was created and who created it, and this is similar to the 'verifiable computational results' (VCR) concept proposed by Gavish and Donoho. Both accountable units and VCRs are version controlled, sharable, and can be incorporated into a collaborative project. However, accountable units use file hashes and do not involve watermarking or public repositories like VCRs. Reproducing collaborative work may be highly complex, requiring repeating computations on multiple systems from multiple authors; however, determining the provenance of each unit is simpler, requiring only a search using file hashes and version control systems.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261481/pdf/nihms962940.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36787790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A System for an Accountable Data Analysis Process in R R中负责数据分析过程的系统

IF 2.1 4区计算机科学

R Journal Pub Date : 2018-05-15 DOI: 10.32614/RJ-2018-001

J. Gelfond, M. Goros, B. Hernandez, A. Bokov

{"title":"A System for an Accountable Data Analysis Process in R","authors":"J. Gelfond, M. Goros, B. Hernandez, A. Bokov","doi":"10.32614/RJ-2018-001","DOIUrl":"https://doi.org/10.32614/RJ-2018-001","url":null,"abstract":"Efficiently producing transparent analyses may be difficult for beginners or tedious for the experienced. This implies a need for computing systems and environments that can efficiently satisfy reproducibility and accountability standards. To this end, we have developed a system, R package, and R Shiny application called adapr (Accountable Data Analysis Process in R) that is built on the principle of accountable units. An accountable unit is a data file (statistic, table or graphic) that can be associated with a provenance, meaning how it was created, when it was created and who created it, and this is similar to the 'verifiable computational results' (VCR) concept proposed by Gavish and Donoho. Both accountable units and VCRs are version controlled, sharable, and can be incorporated into a collaborative project. However, accountable units use file hashes and do not involve watermarking or public repositories like VCRs. Reproducing collaborative work may be highly complex, requiring repeating computations on multiple systems from multiple authors; however, determining the provenance of each unit is simpler, requiring only a search using file hashes and version control systems.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2018-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49470970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

R Package imputeTestbench to Compare Imputation Methods for Univariate Time Series. R包imputeTestbench来比较单变量时间序列的Imputation方法。

IF 2.1 4区计算机科学

R Journal Pub Date : 2018-01-01

Marcus W Beck, Neeraj Bokde, Gualberto Asencio-Cortés, Kishore Kulat

{"title":"R Package imputeTestbench to Compare Imputation Methods for Univariate Time Series.","authors":"Marcus W Beck, Neeraj Bokde, Gualberto Asencio-Cortés, Kishore Kulat","doi":"","DOIUrl":"","url":null,"abstract":"Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The imputeTestbench package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset. Missing data are simulated by removing observations completely at random or in blocks of different sizes depending on characteristics of the data. Several imputation algorithms are included with the package that vary from simple replacement with means to more complex interpolation methods. The testbench is not limited to the default functions and users can add or remove methods as needed. Plotting functions also allow comparative visualization of the behavior and effectiveness of different algorithms. We present example applications that demonstrate how the package can be used to understand differences in prediction accuracy between methods as affected by characteristics of a dataset and the nature of missing data.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6309171/pdf/nihms-1507947.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36822605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PanJen: An R package for Ranking Transformations in a Linear Regression PanJen:一个用于线性回归中排序变换的R包

IF 2.1 4区计算机科学

R Journal Pub Date : 2018-01-01 DOI: 10.32614/RJ-2018-018

C. U. Jensen, T. Panduro

引用次数: 1