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Journal articles on the topic 'Package R'

Author: Grafiati
Published: 26 October 2024
Last updated: 31 July 2025

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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
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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 an
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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 an
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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
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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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Lathrop, Quinn N. "R Package cacIRT." Applied Psychological Measurement 38, no. 7 (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 (2015): 239–40. http://dx.doi.org/10.1177/0146621614567940.

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Barve, Vijay. "Taxonomy Compilation & Curation Within R." Biodiversity Information Science and Standards 5 (August 31, 2021): e73736. https://doi.org/10.3897/biss.5.73736.

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Research projects in ecology or biodiversity either start with an area of study or a target species list. Working with these species lists or taxonomic lists is not as straightforward as it seems. The taxonomic names that are considered to be "standard," are surprisingly dynamic. Over time, the names keep changing with ongoing research and advancements in taxonomy. Additionally, they undergo all sorts of reorganization, such as one species being split into multiple species and/or subspecies, the grouping of multiple species into a single species, and the reclassification of species from one ge
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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 (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 explor
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Wiberg, Marie. "equateIRT Package in R." Measurement: Interdisciplinary Research and Perspectives 16, no. 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 (2019): 211–19. http://dx.doi.org/10.1080/15366367.2019.1600839.

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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 (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 regressio
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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 (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 lic
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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 allo
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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 C
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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 (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
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Gu, Zuguang, and Daniel Hübschmann. "Make Interactive Complex Heatmaps in R." Bioinformatics 38, no. 5 (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
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Bulut, Hasan. "AN R PACKAGE FOR MULTIVARIATE HYPOTHESIS TESTS: MVTESTS." E-journal of New World Sciences Academy 14, no. 4 (2019): 132–38. http://dx.doi.org/10.12739/nwsa.2019.14.4.2a0175.

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Astagneau, Paul C., Guillaume Thirel, Olivier Delaigue, et al. "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 (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
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Hengstberger-Sims, Cecily, and Margaret A. McMillan. "Problem-based learning packages: considerations for neophyte package writers." Nurse Education Today 13, no. 1 (1993): 73–77. http://dx.doi.org/10.1016/0260-6917(93)90013-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 (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)
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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 (2014): 51–61. http://dx.doi.org/10.1080/03078698.2014.933053.

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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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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 ht
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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 ht
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chassagnol, bastien. "Gaussian Mixture Models in R." R Journal 2023-2, no. 2023-2 (2023): 56–76. https://doi.org/10.32614/RJ-2023-043.

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  Gaussian mixture models (GMMs) are widely used for modelling stochastic problems. Indeed, a wide diversity of packages have been developed in R. However, no recent review describing the main features offered by these packages and comparing their performances has been performed. In this article, we first introduce GMMs and the EM algorithm used to retrieve the parameters of the model and analyse the main features implemented among seven of the most widely used R packages. We then empirically compare their statistical and computational performances in relation with the choice of the initi
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Eckenrode, Kelly B., Dario Righelli, Marcel Ramos, et al. "Curated single cell multimodal landmark datasets for R/Bioconductor." PLOS Computational Biology 19, no. 8 (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, ECC
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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 (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
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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 si
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Mínguez, Román, Roberto Basile, and María Durbán. "Pspatreg: R Package for Semiparametric Spatial Autoregressive Models." Mathematics 12, no. 22 (2024): 3598. http://dx.doi.org/10.3390/math12223598.

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This article introduces the R package pspatreg, which is publicly available for download from the Comprehensive R Archive Network, for estimating semiparametric spatial econometric penalized spline (P-Spline) models. These models can incorporate a nonparametric spatiotemporal trend, a spatial lag of the dependent variable, independent variables, noise, and time-series autoregressive noise. The primary functions in this package cover the estimation of P-Spline spatial econometric models using either Restricted Maximum Likelihood (REML) or Maximum Likelihood (ML) methods, as well as the computat
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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 (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 a
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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 (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 b
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Silva, Tiago Chedraoui, Antonio Colaprico, Catharina Olsen, et al. "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 packag
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Lakićević, Milena. "Creating Maps in R (Case Study: National Park “Fruška Gora”)." Contemporary Agriculture 70, no. 1-2 (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 question
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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 planni
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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 conti
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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. W
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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. W
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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 seri
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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 seri
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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 (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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Chevallier, Lennart, and Søren Wichmann. "Note on the ‘toponym’ R Package." Names 72, no. 3 (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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Laurinec, Peter. "TSrepr R package: Time Series Representations." Journal of Open Source Software 3, no. 23 (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 (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 o
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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 (2023): 4365. http://dx.doi.org/10.21105/joss.04365.

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