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Journal articles on the topic 'Package R'

Author: Grafiati

Published: 26 October 2024

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1

Kosztyán, Zsolt Tibor, and Aamir Saghir. "{MFPP(R). An R package for matrix-based flexible project planning." F1000Research 13 (September 12, 2024): 356. http://dx.doi.org/10.12688/f1000research.143144.2.

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Project planning and scheduling are essential parts of project management. While project planning and scheduling tools are already available to support traditional project management approaches, flexible project management approaches, such as agile, extreme, and hybrid project planning, are less well supported by software tools, especially freely available software packages. To our knowledge, no existing R package for project planning and scheduling can support flexible projects. This paper aims to fill this gap by introducing and describing the R package mfpp for matrix-based flexible project planning/scheduling. This package includes a comprehensive set of tools for project managers to schedule both traditional and flexible project plans. The use of the package is illustrated through examples.

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Spurek, P., K. Kamieniecki, J. Tabor, K. Misztal, and M. Śmieja. "R Package CEC." Neurocomputing 237 (May 2017): 410–13. http://dx.doi.org/10.1016/j.neucom.2016.08.118.

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3

Lathrop, Quinn N. "R Package cacIRT." Applied Psychological Measurement 38, no. 7 (July 15, 2014): 581–82. http://dx.doi.org/10.1177/0146621614536465.

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Choi, Seung W., and David R. King. "R Package MAT." Applied Psychological Measurement 39, no. 3 (January 12, 2015): 239–40. http://dx.doi.org/10.1177/0146621614567940.

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Li, Yan, Matthew Sperrin, and Tjeerd van Staa. "R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R." F1000Research 8 (December 23, 2019): 2139. http://dx.doi.org/10.12688/f1000research.21679.1.

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Cardiovascular disease has been the leading cause of death for decades. Risk prediction models are used to identify high risk patients; the most common model used in the UK is ClinRisk’s QRISK3. In this paper we describe the implementation of the QRISK3 algorithm into an R package. The package was successfully validated by the open sourced QRISK3 algorithm and QRISK3 SAS program. We provide detailed examples of the use of the package, including assigning QRISK3 scores for a large cohort of patients. This R package could help the research community to better understand risk prediction scores and improve future risk prediction models. The package is available from CRAN: https://cran.r-project.org/web/packages/QRISK3/index.html.

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Li, Yan, Matthew Sperrin, and Tjeerd van Staa. "R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R." F1000Research 8 (February 28, 2020): 2139. http://dx.doi.org/10.12688/f1000research.21679.2.

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Cardiovascular disease has been the leading cause of death for decades. Risk prediction models are used to identify high risk patients; the most common model used in the UK is ClinRisk’s QRISK3. In this paper we describe the implementation of the QRISK3 algorithm into an R package. The package was successfully validated by the open sourced QRISK3 algorithm and QRISK3 SAS program. We provide detailed examples of the use of the package, including assigning QRISK3 scores for a large cohort of patients. This R package could help the research community to better understand risk prediction scores and improve future risk prediction models. The package is available from CRAN: https://cran.r-project.org/web/packages/QRISK3/index.html.

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Li, Yan, Matthew Sperrin, and Tjeerd van Staa. "R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R." F1000Research 8 (May 22, 2020): 2139. http://dx.doi.org/10.12688/f1000research.21679.3.

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Cardiovascular disease has been the leading cause of death for decades. Risk prediction models are used to identify high risk patients; the most common model used in the UK is ClinRisk’s QRISK3. In this paper we describe the implementation of the QRISK3 algorithm into an R package. The package was successfully validated by the open sourced QRISK3 algorithm and QRISK3 SAS program. We provide detailed examples of the use of the package, including assigning QRISK3 scores for a large cohort of patients. This R package could help the research community to improve future risk prediction models based on a currently used risk prediction model. The package is available from CRAN: https://cran.r-project.org/web/packages/QRISK3/index.html.

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Wendt, Caroline J., and G. Brooke Anderson. "Ten simple rules for finding and selecting R packages." PLOS Computational Biology 18, no. 3 (March 24, 2022): e1009884. http://dx.doi.org/10.1371/journal.pcbi.1009884.

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R is an increasingly preferred software environment for data analytics and statistical computing among scientists and practitioners. Packages markedly extend R’s utility and ameliorate inefficient solutions to data science problems. We outline 10 simple rules for finding relevant packages and determining which package is best for your desired use. We begin in Rule 1 with tips on how to consider your purpose, which will guide your search to follow, where, in Rule 2, you’ll learn best practices for finding and collecting options. Rules 3 and 4 will help you navigate packages’ profiles and explore the extent of their online resources, so that you can be confident in the quality of the package you choose and assured that you’ll be able to access support. In Rules 5 and 6, you’ll become familiar with how the R Community evaluates packages and learn how to assess the popularity and utility of packages for yourself. Rules 7 and 8 will teach you how to investigate and track package development processes, so you can further evaluate their merit. We end in Rules 9 and 10 with more hands-on approaches, which involve digging into package code.

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9

Wiberg, Marie. "equateIRT Package in R." Measurement: Interdisciplinary Research and Perspectives 16, no. 3 (July 3, 2018): 195–202. http://dx.doi.org/10.1080/15366367.2018.1492866.

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Sheng, Yanyan. "CTT Package in R." Measurement: Interdisciplinary Research and Perspectives 17, no. 4 (October 2, 2019): 211–19. http://dx.doi.org/10.1080/15366367.2019.1600839.

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Kyritsis, Konstantinos A., Bing Wang, Julie Sullivan, Rachel Lyne, and Gos Micklem. "InterMineR: an R package for InterMine databases." Bioinformatics 35, no. 17 (January 22, 2019): 3206–7. http://dx.doi.org/10.1093/bioinformatics/btz039.

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Abstract Summary InterMineR is a package designed to provide a flexible interface between the R programming environment and biological databases built using the InterMine platform. The package offers access to the flexible query builder and the library of term enrichment tools of the InterMine framework, as well as interoperability with other Bioconductor packages. This facilitates automation of data retrieval tasks as well as downstream analysis with existing statistical tools in the R environment. Availability and implementation InterMineR is free and open source, released under the LGPL licence and available from the Bioconductor project and Github (https://bioconductor.org/packages/release/bioc/html/InterMineR.html, https://github.com/intermine/interMineR). Supplementary information Supplementary data are available at Bioinformatics online.

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Amir, Arfan Shalihin, Muhammad Arif Tiro, and Ruliana. "Development of R Package for Regression Analysis with User Friendly Interface." ARRUS Journal of Mathematics and Applied Science 2, no. 1 (February 8, 2022): 23–35. http://dx.doi.org/10.35877/mathscience728.

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The use of pirated software in Indonesia is quite high compared to other countries in the world. One of the efforts made to reduce the level of software piracy is to develop publicly licensed software such as R software which is open source software. The preparation of this package uses the R software and other additional packages, especially packages for regression analysis. Making this package can make it easier for users to perform regression analysis easily and legally. This package is named SLR App (Simple Linear Regression App) and MLR App (Multiple Linear Regression) which are regression analysis packages that have a user friendly interface. From the tests carried out that this package has similarities from the results of the analysis between the SLR App and MLR App.

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Gonçalves, Emanuel, and Julio Saez-Rodriguez. "Cyrface: An interface from Cytoscape to R that provides a user interface to R packages." F1000Research 2 (September 19, 2013): 192. http://dx.doi.org/10.12688/f1000research.2-192.v1.

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There is an increasing number of software packages to analyse biological experimental data in the R environment. In particular, Bioconductor, a repository of curated R packages, is one of the most comprehensive resources for bioinformatics and biostatistics. The use of these packages is increasing, but it requires a basic understanding of the R language, as well as the syntax of the specific package used. The availability of user graphical interfaces for these packages would decrease the learning curve and broaden their application. Here, we present a Cytoscape plug-in termed Cyrface that allows Cytoscape plug-ins to connect to any function and package developed in R. Cyrface can be used to run R packages from within the Cytoscape environment making use of a graphical user interface. Moreover, it links the R packages with the capabilities of Cytoscape and its plug-ins, in particular network visualization and analysis. Cyrface’s utility has been demonstrated for two Bioconductor packages (CellNOptR and DrugVsDisease), and here we further illustrate its usage by implementing a workflow of data analysis and visualization. Download links, installation instructions and user guides can be accessed from the Cyrface homepage (http://www.ebi.ac.uk/saezrodriguez/cyrface/).

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Lai, Jiangshan, Dongfang Cui, Weijie Zhu, and Lingfeng Mao. "The Use of R and R Packages in Biodiversity Conservation Research." Diversity 15, no. 12 (December 7, 2023): 1202. http://dx.doi.org/10.3390/d15121202.

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R is one of the most powerful programming languages for conducting data analysis, modeling, and visualization. Although it is widely utilized in biodiversity conservation research, the comprehensive trends in R and R package usage and patterns in the field still remain unexplored. We conducted a comprehensive analysis of R and R package usage frequencies spanning fifteen years, from 2008 to 2022, encompassing over 24,100 research articles published in eight top biodiversity conservation journals. Within this extensive dataset, 10,220 articles (42.3% of the total) explicitly utilized R for data analysis. The use ratio of R demonstrated a consistent linear growth, escalating from 11.1% in 2008 to an impressive 70.6% in 2022. The ten top utilized R packages were vegan, lme4, MuMIn, nlme, mgcv, raster, MASS, ggplot2, car, and dismo. The frequency of R package utilization varied among journals, underscoring the distinct emphases each journal places on specific focuses of biodiversity conservation research. This analysis highlights the pivotal role of R, with its powerful statistical and data visualization capabilities, in empowering researchers to conduct in-depth analyses and gain comprehensive insights into various dimensions of biodiversity conservation science.

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Gonçalves, Emanuel, Franz Mirlach, and Julio Saez-Rodriguez. "Cyrface: An interface from Cytoscape to R that provides a user interface to R packages." F1000Research 2 (July 1, 2014): 192. http://dx.doi.org/10.12688/f1000research.2-192.v2.

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There is an increasing number of software packages to analyse biological experimental data in the R environment. In particular, Bioconductor, a repository of curated R packages, is one of the most comprehensive resources for bioinformatics and biostatistics. The use of these packages is increasing, but it requires a basic understanding of the R language, as well as the syntax of the specific package used. The availability of user graphical interfaces for these packages would decrease the learning curve and broaden their application. Here, we present a Cytoscape app termed Cyrface that allows Cytoscape apps to connect to any function and package developed in R. Cyrface can be used to run R packages from within the Cytoscape environment making use of a graphical user interface. Moreover, it can link R packages with the capabilities of Cytoscape and its apps, in particular network visualization and analysis. Cyrface’s utility has been demonstrated for two Bioconductor packages (CellNOptR and DrugVsDisease), and here we further illustrate its usage by implementing a workflow of data analysis and visualization. Download links, installation instructions and user guides can be accessed from the Cyrface’s homepage (http://www.ebi.ac.uk/saezrodriguez/cyrface/) and from the Cytoscape app store (http://apps.cytoscape.org/apps/cyrface).

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Gu, Zuguang, and Daniel Hübschmann. "Make Interactive Complex Heatmaps in R." Bioinformatics 38, no. 5 (December 2, 2021): 1460–62. http://dx.doi.org/10.1093/bioinformatics/btab806.

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Abstract Summary Heatmap is a powerful visualization method on two-dimensional data to reveal patterns shared by subsets of rows and columns. In this work, we introduce a new R package InteractiveComplexHeatmap that brings interactivity to the widely used ComplexHeatmap package. InteractiveComplexHeatmap is designed with an easy-to-use interface where static complex heatmaps can be directly exported to an interactive Shiny web application only with one additional line of code. InteractiveComplexHeatmap also provides flexible functionalities for integrating interactive heatmap widgets to build more complex and customized Shiny web applications. Availability and implementation The InteractiveComplexHeatmap package and documentations are freely available from the Bioconductor project: https://bioconductor.org/packages/InteractiveComplexHeatmap/. A complete and printer-friendly version of the documentation can also be found in Supplementary File S1. Supplementary information Supplementary data are available at Bioinformatics online.

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17

Bulut, Hasan. "AN R PACKAGE FOR MULTIVARIATE HYPOTHESIS TESTS: MVTESTS." E-journal of New World Sciences Academy 14, no. 4 (November 1, 2019): 132–38. http://dx.doi.org/10.12739/nwsa.2019.14.4.2a0175.

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Silge, Julia, John,C Nash, and Spencer Graves. "Navigating the R Package Universe." R Journal 10, no. 2 (2019): 558. http://dx.doi.org/10.32614/rj-2018-058.

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Hankin, Robin K. S. "Introducing the permutations R package." SoftwareX 11 (January 2020): 100453. http://dx.doi.org/10.1016/j.softx.2020.100453.

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Korner-Nievergelt, Fränzi, and Robert A. Robinson. "Introducing the R-package ‘birdring’." Ringing & Migration 29, no. 1 (January 2, 2014): 51–61. http://dx.doi.org/10.1080/03078698.2014.933053.

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21

Kucheryavskiy, Sergey. "mdatools – R package for chemometrics." Chemometrics and Intelligent Laboratory Systems 198 (March 2020): 103937. http://dx.doi.org/10.1016/j.chemolab.2020.103937.

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22

Hengstberger-Sims, Cecily, and Margaret A. McMillan. "Problem-based learning packages: considerations for neophyte package writers." Nurse Education Today 13, no. 1 (February 1993): 73–77. http://dx.doi.org/10.1016/0260-6917(93)90013-r.

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23

Astagneau, Paul C., Guillaume Thirel, Olivier Delaigue, Joseph H. A. Guillaume, Juraj Parajka, Claudia C. Brauer, Alberto Viglione, Wouter Buytaert, and Keith J. Beven. "Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective." Hydrology and Earth System Sciences 25, no. 7 (July 8, 2021): 3937–73. http://dx.doi.org/10.5194/hess-25-3937-2021.

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Abstract. Following the rise of R as a scientific programming language, the increasing requirement for more transferable research and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available as an open-source resource to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall–runoff modelling from a user perspective by contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated into these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps to inform the selection of which packages could/should be used depending on the problem at hand. In this regard, the technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R.

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Victor Oribamise, B., and Lauren L. Hulsman Hanna. "37 Sibs: an R toolkit for computation of relatedness measures using large pedigrees." Journal of Animal Science 98, Supplement_3 (November 2, 2020): 41–42. http://dx.doi.org/10.1093/jas/skaa054.074.

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Abstract Without appropriate relationships present in a given population, identifying dominance effects in the expression of desirable traits is challenging. Including non-additive effects is desirable to increase accuracy of breeding values. There is no current user-friendly tool package to investigate genetic relatedness in large pedigrees. The objective was to develop and implement efficient algorithms in R to calculate and visualize measures of relatedness (e.g., sibling and family structure, numerator relationship matrices) for large pedigrees. Comparisons to current R packages (Table 1) are also made. Functions to assign animals to families, summary of sibling counts, calculation of numerator relationship matrix (NRM), and NRM summary by groups were created, providing a comprehensive toolkit (Sibs package) not found in other packages. Pedigrees of various sizes (n = 20, 4,035, 120,000 and 132,833) were used to test functionality and compare to current packages. All runs were conducted on a Windows-based computer with an 8 GB RAM, 2.5 GHz Intel Core i7 processor. Other packages had no significant difference in runtime when constructing the NRM for small pedigrees (n = 20) compared to Sibs (0 to 0.05 s difference). However, packages such as ggroups, AGHmatrix, and pedigree were 10 to 15 min slower than Sibs for a 4,035-individual pedigree. Packages nadiv and pedigreemm competed with Sibs (0.30 to 60 s slower than Sibs), but no package besides Sibs was able to complete the 132,833-individual pedigree due to memory allocation issues in R. The nadiv package was closest with a pedigree of 120,000 individuals, but took 37 min to complete (13 min slower than Sibs). This package also provides easier input of pedigrees and is more encompassing of such relatedness measures than other packages (Table 1). Furthermore, it can provide an option to utilize other packages such as GCA for connectedness calculations when using large pedigrees.

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Kaleb, Klara, Alex Warwick Vesztrocy, Adrian Altenhoff, and Christophe Dessimoz. "Expanding the Orthologous Matrix (OMA) programmatic interfaces: REST API and the OmaDB packages for R and Python." F1000Research 8 (January 10, 2019): 42. http://dx.doi.org/10.12688/f1000research.17548.1.

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The Orthologous Matrix (OMA) is a well-established resource to identify orthologs among many genomes. Here, we present two recent additions to its programmatic interface, namely a REST API, and user-friendly R and Python packages called OmaDB. These should further facilitate the incorporation of OMA data into computational scripts and pipelines. The REST API can be freely accessed at https://omabrowser.org/api. The R OmaDB package is available as part of Bioconductor at http://bioconductor.org/packages/OmaDB/, and the omadb Python package is available from the Python Package Index (PyPI) at https://pypi.org/project/omadb/.

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Kaleb, Klara, Alex Warwick Vesztrocy, Adrian Altenhoff, and Christophe Dessimoz. "Expanding the Orthologous Matrix (OMA) programmatic interfaces: REST API and the OmaDB packages for R and Python." F1000Research 8 (March 29, 2019): 42. http://dx.doi.org/10.12688/f1000research.17548.2.

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The Orthologous Matrix (OMA) is a well-established resource to identify orthologs among many genomes. Here, we present two recent additions to its programmatic interface, namely a REST API, and user-friendly R and Python packages called OmaDB. These should further facilitate the incorporation of OMA data into computational scripts and pipelines. The REST API can be freely accessed at https://omabrowser.org/api. The R OmaDB package is available as part of Bioconductor at http://bioconductor.org/packages/OmaDB/, and the omadb Python package is available from the Python Package Index (PyPI) at https://pypi.org/project/omadb/.

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27

Eckenrode, Kelly B., Dario Righelli, Marcel Ramos, Ricard Argelaguet, Christophe Vanderaa, Ludwig Geistlinger, Aedin C. Culhane, et al. "Curated single cell multimodal landmark datasets for R/Bioconductor." PLOS Computational Biology 19, no. 8 (August 25, 2023): e1011324. http://dx.doi.org/10.1371/journal.pcbi.1011324.

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Background The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. Results We collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor’s Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor’s ecosystem of hundreds of packages for single-cell and multimodal data. Conclusions We provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.

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Li, Pumin, Qi Xu, Xu Hua, Zhongwei Xie, Jie Li, and Jin Wang. "primirTSS: an R package for identifying cell-specific microRNA transcription start sites." Bioinformatics 36, no. 11 (March 14, 2020): 3605–6. http://dx.doi.org/10.1093/bioinformatics/btaa173.

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Abstract Summary The R/Bioconductor package primirTSS is a fast and convenient tool that allows implementation of the analytical method to identify transcription start sites of microRNAs by integrating ChIP-seq data of H3K4me3 and Pol II. It further ensures the precision by employing the conservation score and sequence features. The tool showed a good performance when using H3K4me3 or Pol II Chip-seq data alone as input, which brings convenience to applications where multiple datasets are hard to acquire. This flexible package is provided with both R-programming interfaces as well as graphical web interfaces. Availability and implementation primirTSS is available at: http://bioconductor.org/packages/primirTSS. The documentation of the package including an accompanying tutorial was deposited at: https://bioconductor.org/packages/release/bioc/vignettes/primirTSS/inst/doc/primirTSS.html. Contact jwang@nju.edu.cn Supplementary information Supplementary data are available at Bioinformatics online.

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Borcherding, Nicholas, and Nicholas L. Bormann. "scRepertoire: An R-based toolkit for single-cell immune receptor analysis." F1000Research 9 (January 27, 2020): 47. http://dx.doi.org/10.12688/f1000research.22139.1.

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Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine mRNA and immune profiling, scRepertoire was built to process data derived from 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat R package. The scRepertoire R package and processed data are open source and available on GitHub and provides in-depth tutorials on the capability of the package.

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Kort, Eric J., and Stefan Jovinge. "Streamlined analysis of LINCS L1000 data with the slinky package for R." Bioinformatics 35, no. 17 (January 10, 2019): 3176–77. http://dx.doi.org/10.1093/bioinformatics/btz002.

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Abstract Summary The L1000 dataset from the NIH LINCS program holds the promise to deconvolute a wide range of biological questions in transcriptional space. However, using this large and decentralized dataset presents its own challenges. The slinky package was created to streamline the process of identifying samples of interest and their corresponding control samples, and loading their associated expression data and metadata. The package can integrate with workflows leveraging the BioConductor collection of tools by encapsulating the L1000 data as a SummarizedExperiment object. Availability and implementation Slinky is freely available as an R package at http://bioconductor.org/packages/slinky

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Cuadrado-Gallego, Juan J., Josefa Gómez, Abdelhamid Tayebi, Luis Usero, Carlos J. Hellín, and Adrián Valledor. "LearningRlab: Educational R Package for Statistics in Computer Science Engineering." Sustainability 15, no. 10 (May 18, 2023): 8246. http://dx.doi.org/10.3390/su15108246.

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This paper describes and evaluates the educational interest of LearningRlab, an educational R package developed for teaching statistics in computer science engineering. The package was developed by final degree project students to be used as an educational environment for statistics students who evaluated the package and provided feedback for future versions. Such a process increases the motivation of both groups of students. This paper presents how the use of the R packages conceived and developed for engineering education can improve the learning process in the computer science engineering bachelor’s degree. Two different evaluations, one performed by a group of statistics students, and the other performed by final degree project students, were used to evaluate the impact on the learning process of the first version of the package to develop the second version of the package, which corrects and enhances the first version. The evaluation results show a positive effect on the learning process in both subjects. The analysis of the learning outcomes reflected in the grades of the experimental and control groups demonstrates that LearningRlab can be used as a teaching aid for statistics and final degree project subjects of the computer science engineering degree. The average laboratory grade of the students who used the package (5.76) was noticeably higher than the average laboratory grade of students who did not use it (1.84).

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Tsagris, Michail, and Ioannis Tsamardinos. "Feature selection with the R package MXM." F1000Research 7 (September 30, 2019): 1505. http://dx.doi.org/10.12688/f1000research.16216.2.

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Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly available R as packages while offering few options. The R package MXM offers a variety of feature selection algorithms, and has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models that can be plugged into the feature selection algorithms (for example with time to event data the user can choose among Cox, Weibull, log logistic or exponential regression); c) it includes an algorithm for detecting multiple solutions (many sets of statistically equivalent features, plain speaking, two features can carry statistically equivalent information when substituting one with the other does not effect the inference or the conclusions); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R (In a 16GB RAM terminal for example, R cannot directly load data of 16GB size. By utilizing the proper package, we load the data and then perform feature selection.). In this paper, we qualitatively compare MXM with other relevant feature selection packages and discuss its advantages and disadvantages. Further, we provide a demonstration of MXM’s algorithms using real high-dimensional data from various applications.

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Silva, Tiago Chedraoui, Antonio Colaprico, Catharina Olsen, Tathiane M. Malta, Gianluca Bontempi, Michele Ceccarelli, Benjamin P. Berman, and Houtan Noushmehr. "TCGAbiolinksGUI: A graphical user interface to analyze cancer molecular and clinical data." F1000Research 7 (April 10, 2018): 439. http://dx.doi.org/10.12688/f1000research.14197.1.

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The GDC (Genomic Data Commons) data portal provides users with data from cancer genomics studies. Recently, we developed the R/Bioconductor TCGAbiolinks package, which allows users to search, download and prepare cancer genomics data for integrative data analysis. The use of this package requires users to have advanced knowledge of R thus limiting the number of users. To overcome this obstacle and improve the accessibility of the package by a wider range of users, we developed a graphical user interface (GUI) using Shiny available through the package TCGAbiolinksGUI. The TCGAbiolinksGUI package is freely available within the Bioconductor project at http://bioconductor.org/packages/TCGAbiolinksGUI/. Links to the GitHub repository, a demo version of the tool, a docker image and PDF/video tutorials are available from the TCGAbiolinksGUI site.

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Kosztyán, Zsolt Tibor, and Aamir Saghir. "{MFPP(R). An R package for matrix-based flexible project planning." F1000Research 13 (April 23, 2024): 356. http://dx.doi.org/10.12688/f1000research.143144.1.

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Project planning and scheduling are essential parts of project management. While project planning and scheduling tools are already available to support traditional project management approaches, flexible project management, such as agile, extreme and hybrid project planning, are only somewhat supported by computer algorithms. To the best of our knowledge, no existing R package for project planning and scheduling can support project planning and scheduling for flexible projects. In this paper, the goal is to fill this gap; to this end, the R package mfpp for matrix-based flexible project planning/scheduling is introduced and described. This package includes a comprehensive set of tools for project managers to schedule both traditional and flexible project plans. The use of the package is illustrated through examples.

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Lakićević, Milena. "Creating Maps in R (Case Study: National Park “Fruška Gora”)." Contemporary Agriculture 70, no. 1-2 (May 26, 2021): 41–45. http://dx.doi.org/10.2478/contagri-2021-0008.

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Summary This paper aims to present the possibilities for creating maps in the programming language R. Even though R is primarily developed as a statistical program, its application in the area of mapping and spatial statistics is becoming frequent and highly relevant. Many R packages make the mapping process easier and user-friendly, and this paper presents the most commonly used ones: “leaflet”, “ggplot2” and “ggmap”. The selection of the R package depends on the user’s proficiency in R programming but also depends on the visual quality of the map the user wants to gain. Based on the questionnaire conducted in this research, the paper recommends application of the “leaflet” package for the beginners, the “ggplot2” package for medium proficient users, and the “ggmap” package for the most advanced R users. After creating maps in R it is possible to conduct additional analysis related to processing of the spatial data contained within, and this would be a recommendation for future research. In this paper, the mapping process is demonstrated on the case study of the National Park “Fruška gora” in Serbia, and different types of maps are presented.

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Cambon, Jesse, Diego Hernangómez, Christopher Belanger, and Daniel Possenriede. "tidygeocoder: An R package for geocoding." Journal of Open Source Software 6, no. 65 (September 9, 2021): 3544. http://dx.doi.org/10.21105/joss.03544.

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Vidoni, Melina. "Understanding Roxygen package documentation in R." Journal of Systems and Software 188 (June 2022): 111265. http://dx.doi.org/10.1016/j.jss.2022.111265.

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Probst, Philipp, Quay Au, Giuseppe Casalicchio, Clemens Stachl, and Bernd Bischl. "Multilabel Classification with R Package mlr." R Journal 9, no. 1 (2017): 352. http://dx.doi.org/10.32614/rj-2017-012.

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Laurinec, Peter. "TSrepr R package: Time Series Representations." Journal of Open Source Software 3, no. 23 (March 12, 2018): 577. http://dx.doi.org/10.21105/joss.00577.

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Arnhold, Emmanuel. "R-environment package for regression analysis." Pesquisa Agropecuária Brasileira 53, no. 7 (July 2018): 870–73. http://dx.doi.org/10.1590/s0100-204x2018000700012.

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Abstract: The objective of this work was to develop a package in the R environment for automating and facilitating the regression analysis. Named easyreg, the package offers five functions. The er1 function performs analyses in 13 models, including linear, nonlinear, and mixed models. The er2 function considers the lack of fit in the analyses and in the following designs: completely randomized, randomized complete block, Latin squares, and repeated Latin squares. The regplot function generates graphics; the bl function estimates two-segment models; and the regtest function tests the equality of parameters and the identity of the models. These functions allow of a great number of analyses and confer practicality and versatility to the regression analysis.

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Arandia, Ernesto, and Bradley J. Eck. "An R package for EPANET simulations." Environmental Modelling & Software 107 (September 2018): 59–63. http://dx.doi.org/10.1016/j.envsoft.2018.05.016.

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Barbu, Vlad Stefan, Florian Lecocq, Corentin Lothodé, and Nicolas Vergne. "smmR: A Semi-Markov R package." Journal of Open Source Software 8, no. 85 (May 8, 2023): 4365. http://dx.doi.org/10.21105/joss.04365.

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Chevallier, Lennart, and Søren Wichmann. "Note on the ‘toponym’ R Package." Names 72, no. 3 (September 11, 2024): 76–83. http://dx.doi.org/10.5195/names.2024.2617.

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In this note, we describe how to install and use the ‘toponym’ R package, which is designed for mapping and manipulating toponymic data from the GeoNames database. This introduction will allow even unexperienced users of R to efficiently produce maps and perform simple analyses.

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Russell, Pamela H., and Debashis Ghosh. "Radtools: R utilities for smooth navigation of medical image data." F1000Research 7 (December 24, 2018): 1976. http://dx.doi.org/10.12688/f1000research.17139.1.

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The radiology community has adopted several widely used standards for medical image files, including the popular DICOM (Digital Imaging and Communication in Medicine) and NIfTI (Neuroimaging Informatics Technology Initiative) standards. These file formats include image intensities as well as potentially extensive metadata. The NIfTI standard specifies a particular set of header fields describing the image and minimal information about the scan. DICOM headers can include any of >4,000 available metadata attributes spanning a variety of topics. NIfTI files contain all slices for an image series, while DICOM files capture single slices and image series are typically organized into a directory. Each DICOM file contains metadata for the image series as well as the individual image slice. The programming environment R is popular for data analysis due to its free and open code, active ecosystem of tools and users, and excellent system of contributed packages. Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts. However, R is increasing in popularity in this area due to several packages for processing and analysis of image files. While these R packages handle image import and processing, no existing package makes image metadata conveniently accessible. Extracting image metadata, combining across slices, and converting to useful formats can be prohibitively cumbersome, especially for DICOM files. We present radtools, an R package for smooth navigation of medical image data. Radtools makes the problem of extracting image metadata trivially simple, providing simple functions to explore and return information in familiar R data structures. Radtools also facilitates extraction of image data and viewing of image slices. The package is freely available under the MIT license at https://github.com/pamelarussell/radtools and is easily installable from the Comprehensive R Archive Network (https://cran.r-project.org/package=radtools).

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Russell, Pamela H., and Debashis Ghosh. "Radtools: R utilities for convenient extraction of medical image metadata." F1000Research 7 (January 25, 2019): 1976. http://dx.doi.org/10.12688/f1000research.17139.2.

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The radiology community has adopted several widely used standards for medical image files, including the popular DICOM (Digital Imaging and Communication in Medicine) and NIfTI (Neuroimaging Informatics Technology Initiative) standards. These file formats include image intensities as well as potentially extensive metadata. The NIfTI standard specifies a particular set of header fields describing the image and minimal information about the scan. DICOM headers can include any of >4,000 available metadata attributes spanning a variety of topics. NIfTI files contain all slices for an image series, while DICOM files capture single slices and image series are typically organized into a directory. Each DICOM file contains metadata for the image series as well as the individual image slice. The programming environment R is popular for data analysis due to its free and open code, active ecosystem of tools and users, and excellent system of contributed packages. Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts. However, R is increasing in popularity in this area due to several packages for processing and analysis of image files. While these R packages handle image import and processing, no existing package makes image metadata conveniently accessible. Extracting image metadata, combining across slices, and converting to useful formats can be prohibitively cumbersome, especially for DICOM files. We present radtools, an R package for convenient extraction of medical image metadata. Radtools provides simple functions to explore and return metadata in familiar R data structures. For convenience, radtools also includes wrappers of existing tools for extraction of pixel data and viewing of image slices. The package is freely available under the MIT license at https://github.com/pamelarussell/radtools and is easily installable from the Comprehensive R Archive Network (https://cran.r-project.org/package=radtools).

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Foster, Zachary S. L., Scott Chamberlain, and Niklaus J. Grünwald. "Taxa: An R package implementing data standards and methods for taxonomic data." F1000Research 7 (March 5, 2018): 272. http://dx.doi.org/10.12688/f1000research.14013.1.

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The taxa R package provides a set of tools for defining and manipulating taxonomic data. The recent and widespread application of DNA sequencing to community composition studies is making large data sets with taxonomic information commonplace. However, compared to typical tabular data, this information is encoded in many different ways and the hierarchical nature of taxonomic classifications makes it difficult to work with. There are many R packages that use taxonomic data to varying degrees but there is currently no cross-package standard for how this information is encoded and manipulated. We developed the R package taxa to provide a robust and flexible solution to storing and manipulating taxonomic data in R and any application-specific information associated with it. Taxa provides parsers that can read common sources of taxonomic information (taxon IDs, sequence IDs, taxon names, and classifications) from nearly any format while preserving associated data. Once parsed, the taxonomic data and any associated data can be manipulated using a cohesive set of functions modeled after the popular R package dplyr. These functions take into account the hierarchical nature of taxa and can modify the taxonomy or associated data in such a way that both are kept in sync. Taxa is currently being used by the metacoder and taxize packages, which provide broadly useful functionality that we hope will speed adoption by users and developers.

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Foster, Zachary S. L., Scott Chamberlain, and Niklaus J. Grünwald. "Taxa: An R package implementing data standards and methods for taxonomic data." F1000Research 7 (September 11, 2018): 272. http://dx.doi.org/10.12688/f1000research.14013.2.

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The taxa R package provides a set of tools for defining and manipulating taxonomic data. The recent and widespread application of DNA sequencing to community composition studies is making large data sets with taxonomic information commonplace. However, compared to typical tabular data, this information is encoded in many different ways and the hierarchical nature of taxonomic classifications makes it difficult to work with. There are many R packages that use taxonomic data to varying degrees but there is currently no cross-package standard for how this information is encoded and manipulated. We developed the R package taxa to provide a robust and flexible solution to storing and manipulating taxonomic data in R and any application-specific information associated with it. Taxa provides parsers that can read common sources of taxonomic information (taxon IDs, sequence IDs, taxon names, and classifications) from nearly any format while preserving associated data. Once parsed, the taxonomic data and any associated data can be manipulated using a cohesive set of functions modeled after the popular R package dplyr. These functions take into account the hierarchical nature of taxa and can modify the taxonomy or associated data in such a way that both are kept in sync. Taxa is currently being used by the metacoder and taxize packages, which provide broadly useful functionality that we hope will speed adoption by users and developers.

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Chamberlain, Scott A., and Eduard Szöcs. "taxize: taxonomic search and retrieval in R." F1000Research 2 (September 18, 2013): 191. http://dx.doi.org/10.12688/f1000research.2-191.v1.

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All species are hierarchically related to one another, and we use taxonomic names to label the nodes in this hierarchy. Taxonomic data is becoming increasingly available on the web, but scientists need a way to access it in a programmatic fashion that’s easy and reproducible. We have developed taxize, an open-source software package (freely available from http://cran.r-project.org/web/packages/taxize/index.html) for the R language. taxize provides simple, programmatic access to taxonomic data for 13 data sources around the web. We discuss the need for a taxonomic toolbelt in R, and outline a suite of use cases for which taxize is ideally suited (including a full workflow as an appendix). The taxize package facilitates open and reproducible science by allowing taxonomic data collection to be done in the open-source R platform.

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Chamberlain, Scott A., and Eduard Szöcs. "taxize: taxonomic search and retrieval in R." F1000Research 2 (October 28, 2013): 191. http://dx.doi.org/10.12688/f1000research.2-191.v2.

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All species are hierarchically related to one another, and we use taxonomic names to label the nodes in this hierarchy. Taxonomic data is becoming increasingly available on the web, but scientists need a way to access it in a programmatic fashion that’s easy and reproducible. We have developed taxize, an open-source software package (freely available from http://cran.r-project.org/web/packages/taxize/index.html) for the R language. taxize provides simple, programmatic access to taxonomic data for 13 data sources around the web. We discuss the need for a taxonomic toolbelt in R, and outline a suite of use cases for which taxize is ideally suited (including a full workflow as an appendix). The taxize package facilitates open and reproducible science by allowing taxonomic data collection to be done in the open-source R platform.

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Tsagris, Michail, and Ioannis Tsamardinos. "Feature selection with the R package MXM." F1000Research 7 (September 20, 2018): 1505. http://dx.doi.org/10.12688/f1000research.16216.1.

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Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R as a package. The R package MXM is such an example, which not only offers a variety of feature selection algorithms, but has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models to plug into the feature selection algorithms; c) it includes an algorithm for detecting multiple solutions (many sets of equivalent features); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R. In this paper we qualitatively compare MXM with other relevant packages and discuss its advantages and disadvantages. We also provide a demonstration of its algorithms using real high-dimensional data from various applications.

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