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etrm: Energy Trading and Risk Management in R 能源交易与风险管理[j]
IF 2.1 4区 计算机科学
R Journal Pub Date : 2022-01-01 DOI: 10.32614/rj-2022-013
Anders D. Sleire
{"title":"etrm: Energy Trading and Risk Management in R","authors":"Anders D. Sleire","doi":"10.32614/rj-2022-013","DOIUrl":"https://doi.org/10.32614/rj-2022-013","url":null,"abstract":"","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69958723","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: 0
Multivariate Subgaussian Stable Distributions in R R中的多元亚高斯稳定分布
IF 2.1 4区 计算机科学
R Journal Pub Date : 2022-01-01 DOI: 10.32614/rj-2022-056
B. Swihart, J. P. Nolan
{"title":"Multivariate Subgaussian Stable Distributions in R","authors":"B. Swihart, J. P. Nolan","doi":"10.32614/rj-2022-056","DOIUrl":"https://doi.org/10.32614/rj-2022-056","url":null,"abstract":"","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69959039","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: 0
diproperm: An R Package for the DiProPerm Test. diproperm：用于 DiProPerm 测试的 R 软件包。
IF 2.3 4区 计算机科学
R Journal Pub Date : 2021-12-01 Epub Date: 2021-08-17 DOI: 10.32614/rj-2021-072
Andrew G Allmon, J S Marron, Michael G Hudgens
{"title":"diproperm: An R Package for the DiProPerm Test.","authors":"Andrew G Allmon, J S Marron, Michael G Hudgens","doi":"10.32614/rj-2021-072","DOIUrl":"10.32614/rj-2021-072","url":null,"abstract":"<p><p>High-dimensional low sample size (HDLSS) data sets frequently emerge in many biomedical applications. The direction-projection-permutation (DiProPerm) test is a two-sample hypothesis test for comparing two high-dimensional distributions. The DiProPerm test is exact, i.e., the type I error is guaranteed to be controlled at the nominal level for any sample size, and thus is applicable in the HDLSS setting. This paper discusses the key components of the DiProPerm test, introduces the diproperm R package, and demonstrates the package on a real-world data set.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202909/pdf/nihms-1809552.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40026217","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
The R Journal: Changes in R 4.0–4.1 R日志:R 4.0-4.1的变化
IF 2.1 4区 计算机科学
R Journal Pub Date : 2021-07-01 DOI: 10.32614/CORE
T. Kalibera, S. Meyer, K. Hornik
{"title":"The R Journal: Changes in R 4.0–4.1","authors":"T. Kalibera, S. Meyer, K. Hornik","doi":"10.32614/CORE","DOIUrl":"https://doi.org/10.32614/CORE","url":null,"abstract":"","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86262117","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: 0
The R Journal: Editorial R杂志:社论
IF 2.1 4区 计算机科学
R Journal Pub Date : 2021-07-01 DOI: 10.32614/EDITORIAL
D. Cook
{"title":"The R Journal: Editorial","authors":"D. Cook","doi":"10.32614/EDITORIAL","DOIUrl":"https://doi.org/10.32614/EDITORIAL","url":null,"abstract":"","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77716679","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: 0
stratamatch: Prognostic Score Stratification Using a Pilot Design. 分层:使用先导设计的预后评分分层。
IF 2.1 4区 计算机科学
R Journal Pub Date : 2021-06-01 DOI: 10.32614/RJ-2021-063
Rachael C Aikens, Joseph Rigdon, Justin Lee, Michael Baiocchi, Andrew B Goldstone, Peter Chiu, Y Joseph Woo, Jonathan H Chen
{"title":"stratamatch: Prognostic Score Stratification Using a Pilot Design.","authors":"Rachael C Aikens,&nbsp;Joseph Rigdon,&nbsp;Justin Lee,&nbsp;Michael Baiocchi,&nbsp;Andrew B Goldstone,&nbsp;Peter Chiu,&nbsp;Y Joseph Woo,&nbsp;Jonathan H Chen","doi":"10.32614/RJ-2021-063","DOIUrl":"https://doi.org/10.32614/RJ-2021-063","url":null,"abstract":"In a block-randomized controlled trial, individuals are subdivided by prognostically important baseline characteristics (e.g., age group, sex, or smoking status), prior to randomization. This step reduces the heterogeneity between the treatment groups with respect to the baseline factors most important to determining the outcome, thus enabling more precise estimation of treatment effect. The stratamatch package extends this approach to the observational setting by implementing functions to separate an observational data set into strata and interrogate the quality of different stratification schemes. Once an acceptable stratification is found, treated and control individuals can be matched by propensity score within strata, thereby recapitulating the block-randomized trial design for the observational study. The stratification scheme implemented by stratamatch applies a \"pilot design\" approach (Aikens, Greaves, and Baiocchi 2019) to estimate a quantity called the prognostic score (Hansen 2008), which is used to divide individuals into strata. The potential benefits of such an approach are twofold. First, stratifying the data enables more computationally efficient matching of large data sets. Second, methodological studies suggest that using a prognostic score to inform the matching process increases the precision of the effect estimate and reduces sensitivity to bias from unmeasured confounding factors (Aikens et al. 2019; Leacy and Stuart 2014; Antonelli, Cefalu, Palmer, and Agniel 2018). A common mistake is to believe reserving more data for the analysis phase of a study is always better. Instead, the stratamatch approach suggests how clever use of data in the design phase of large studies can lead to major benefits in the robustness of the study conclusions.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273035/pdf/nihms-1619246.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40597502","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: 2
Benchmarking R packages for Calculation of Persistent Homology. 对计算持久同源性的R包进行基准测试。
IF 2.1 4区 计算机科学
R Journal Pub Date : 2021-06-01 Epub Date: 2021-06-07 DOI: 10.32614/RJ-2021-033
Eashwar V Somasundaram, Shael E Brown, Adam Litzler, Jacob G Scott, Raoul R Wadhwa
{"title":"Benchmarking R packages for Calculation of Persistent Homology.","authors":"Eashwar V Somasundaram,&nbsp;Shael E Brown,&nbsp;Adam Litzler,&nbsp;Jacob G Scott,&nbsp;Raoul R Wadhwa","doi":"10.32614/RJ-2021-033","DOIUrl":"https://doi.org/10.32614/RJ-2021-033","url":null,"abstract":"<p><p>Several persistent homology software libraries have been implemented in R. Specifically, the Dionysus, GUDHI, and Ripser libraries have been wrapped by the <b>TDA</b> and <b>TDAstats</b> CRAN packages. These software represent powerful analysis tools that are computationally expensive and, to our knowledge, have not been formally benchmarked. Here, we analyze runtime and memory growth for the 2 R packages and the 3 underlying libraries. We find that datasets with less than 3 dimensions can be evaluated with persistent homology fastest by the GUDHI library in the <b>TDA</b> package. For higher-dimensional datasets, the Ripser library in the TDAstats package is the fastest. Ripser and <b>TDAstats</b> are also the most memory-efficient tools to calculate persistent homology.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434812/pdf/nihms-1733366.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39409270","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: 7
Conference Report of Why R? Turkey 2021 “为什么R?”2021年土耳其
IF 2.1 4区 计算机科学
R Journal Pub Date : 2021-01-01 DOI: 10.32614/RJ-2021
M. Çavuş, Olgun Aydın, Ozan Evkaya, Ozancan Özdemir, Deniz Bezer, Ugur Dar
{"title":"Conference Report of Why R? Turkey 2021","authors":"M. Çavuş, Olgun Aydın, Ozan Evkaya, Ozancan Özdemir, Deniz Bezer, Ugur Dar","doi":"10.32614/RJ-2021","DOIUrl":"https://doi.org/10.32614/RJ-2021","url":null,"abstract":"The Why R? Turkey 2021 as a three-day online conference was organized to bring together researchers and professionals from Turkey on April 16-17-18, 2021. We hereby aimed to promote the R community in Turkey by bringing R users with different backgrounds such as genetics, sociology, finance, economy, bio-statistics. There were 8 thematic sessions and 18 invited speakers. In this article, it is aimed to describe the preparation phase, technical details, and the impact of the conference on audience.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72897054","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: 0
SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting. SurvBoost:一个基于梯度提升的分层比例风险模型中高维变量选择的R包。
IF 2.1 4区 计算机科学
R Journal Pub Date : 2020-06-01 DOI: 10.32614/rj-2020-018
Emily Morris, Kevin He, Yanming Li, Yi Li, Jian Kang
{"title":"SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting.","authors":"Emily Morris,&nbsp;Kevin He,&nbsp;Yanming Li,&nbsp;Yi Li,&nbsp;Jian Kang","doi":"10.32614/rj-2020-018","DOIUrl":"https://doi.org/10.32614/rj-2020-018","url":null,"abstract":"<p><p>High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables that do not satisfy the PH assumption, in which case the stratified proportional hazards (SPH) model can be adopted to control the confounding effects by stratification without directly modeling the confounding effects. However, there is a lack of computationally efficient statistical software for high-dimensional variable selection in the SPH model. In this work an R package, <b>SurvBoost</b>, is developed to implement the gradient boosting algorithm for fitting the SPH model with high-dimensional covariate variables. Simulation studies demonstrate that in many scenarios <b>SurvBoost</b> can achieve better selection accuracy and reduce computational time substantially compared to the existing R package that implements boosting algorithms without stratification. The proposed R package is also illustrated by an analysis of gene expression data with survival outcome in The Cancer Genome Atlas study. In addition, a detailed hands-on tutorial for <b>SurvBoost</b> is provided.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8174798/pdf/nihms-1656432.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39084202","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: 1
CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model. CoxPhLb:一个在Cox模型下分析长度偏差数据的R包。
IF 2.1 4区 计算机科学
R Journal Pub Date : 2020-06-01 DOI: 10.32614/rj-2020-024
Chi Hyun Lee, Heng Zhou, Jing Ning, Diane D Liu, Yu Shen
{"title":"CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model.","authors":"Chi Hyun Lee,&nbsp;Heng Zhou,&nbsp;Jing Ning,&nbsp;Diane D Liu,&nbsp;Yu Shen","doi":"10.32614/rj-2020-024","DOIUrl":"https://doi.org/10.32614/rj-2020-024","url":null,"abstract":"<p><p>Data subject to length-biased sampling are frequently encountered in various applications including prevalent cohort studies and are considered as a special case of left-truncated data under the stationarity assumption. Many semiparametric regression methods have been proposed for length-biased data to model the association between covariates and the survival outcome of interest. In this paper, we present a brief review of the statistical methodologies established for the analysis of length-biased data under the Cox model, which is the most commonly adopted semiparametric model, and introduce an R package <b>CoxPhLb</b> that implements these methods. Specifically, the package includes features such as fitting the Cox model to explore covariate effects on survival times and checking the proportional hazards model assumptions and the stationarity assumption. We illustrate usage of the package with a simulated data example and a real dataset, the Channing House data, which are publicly available.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595345/pdf/nihms-1638580.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38657972","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
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