Готові списки джерел за темами / Package R

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  2. Дисертації
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Добірка наукової літератури з теми "Package R"

Автор: Grafiati
Опубліковано: 26 жовтня 2024

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Статті в журналах з теми "Package R"

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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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2

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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4

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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5

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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Анотація:
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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Анотація:
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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7

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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Анотація:
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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Анотація:
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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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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Дисертації з теми "Package R"

1

Theußl, Stefan, Uwe Ligges, and Kurt Hornik. "Prospects and Challenges in R Package Development." Institute for Statistics and Mathematics, WU Vienna University of Economics and Business, 2010. http://epub.wu.ac.at/866/1/document.pdf.

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Анотація:
R, a software package for statistical computing and graphics, has evolved into the lingua franca of (computational) statistics. One of the cornerstones of R's success is the decentralized and modularized way of creating software using a multi-tiered development model: The R Development Core Team provides the "base system", which delivers basic statistical functionality, and many other developers contribute code in the form of extensions in a standardized format via so-called packages. In order to be accessible by a broader audience, packages are made available via standardized source code repositories. To support such a loosely coupled development model, repositories should be able to verify that the provided packages meet certain formal quality criteria and "work": both relative to the development of the base R system as well as with other packages (interoperability). However, established quality assurance systems and collaborative infrastructures typically face several challenges, some of which we will discuss in this paper.
Series: Research Report Series / Department of Statistics and Mathematics
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2

Guy, Abel. "fanplot: An R Package for Visualising Sequential Distributions." The R Foundation for Statistical Computing, 2015. http://epub.wu.ac.at/5910/1/Abel_2015_RJ_fanplot%2DAn%2DR%2DPackage.pdf.

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Fan charts, first developed by the Bank of England in 1996, have become a standard method for visualising forecasts with uncertainty. Using shading fan charts focus the attention towards the whole distribution away from a single central measure. This article describes the basics of plotting fan charts using an R add-on package alongside some additional methods for displaying sequential distributions. Examples are based on distributions of both estimated parameters from a time series model and future values with uncertainty.
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3

Kastner, Gregor. "Heavy-Tailed Innovations in the R Package stochvol." WU Vienna University of Economics and Business, 2015. http://epub.wu.ac.at/4918/1/heavytails.pdf.

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We document how sampling from a conditional Student's t distribution is implemented in stochvol. Moreover, a simple example using EUR/CHF exchange rates illustrates how to use the augmented sampler. We conclude with results and implications. (author's abstract)
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4

Randahl, David. "Raoul: An R-Package for Handling Missing Data." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297051.

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5

Hornik, Kurt, and Bettina Grün. "topicmodels: An R Package for Fitting Topic Models." American Statistical Association, 2011. http://epub.wu.ac.at/3987/1/topicmodels.pdf.

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Анотація:
Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.
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6

Karatzoglou, Alexandros, Alex Smola, Kurt Hornik, and Achim Zeileis. "kernlab - An S4 package for kernel methods in R." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2004. http://epub.wu.ac.at/1048/1/document.pdf.

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kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 object model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, and a spectral clustering algorithm. Moreover it provides a general purpose quadratic programming solver, and an incomplete Cholesky decomposition method. (author's abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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7

Buder, Thomas, Andreas Deutsch, Michael Seifert, and Anja Voss-Böhme. "CellTrans: An R Package to Quantify Stochastic Cell State Transitions." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-230144.

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Many normal and cancerous cell lines exhibit a stable composition of cells in distinct states which can, e.g., be defined on the basis of cell surface markers. There is evidence that such an equilibrium is associated with stochastic transitions between distinct states. Quantifying these transitions has the potential to better understand cell lineage compositions. We introduce CellTrans, an R package to quantify stochastic cell state transitions from cell state proportion data from fluorescence-activated cell sorting and flow cytometry experiments. The R package is based on a mathematical model in which cell state alterations occur due to stochastic transitions between distinct cell states whose rates only depend on the current state of a cell. CellTrans is an automated tool for estimating the underlying transition probabilities from appropriately prepared data. We point out potential analytical challenges in the quantification of these cell transitions and explain how CellTrans handles them. The applicability of CellTrans is demonstrated on publicly available data on the evolution of cell state compositions in cancer cell lines. We show that CellTrans can be used to (1) infer the transition probabilities between different cell states, (2) predict cell line compositions at a certain time, (3) predict equilibrium cell state compositions, and (4) estimate the time needed to reach this equilibrium. We provide an implementation of CellTrans in R, freely available via GitHub (https://github.com/tbuder/CellTrans).
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8

de, Leeuw Jan, and Patrick Mair. "Simple and Canonical Correspondence Analysis Using the R Package anacor." Foundation for Open Access Statistics, 2009. http://dx.doi.org/10.18637/jss.v031.i05.

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Анотація:
This paper presents the R package anacor for the computation of simple and canonical correspondence analysis with missing values. The canonical correspondence analysis is specified in a rather general way by imposing covariates on the rows and/or the columns of the two-dimensional frequency table. The package allows for scaling methods such as standard, Benzécri, centroid, and Goodman scaling. In addition, along with well-known two- and three-dimensional joint plots including confidence ellipsoids, it offers alternative plotting possibilities in terms of transformation plots, Benzécri plots, and regression plots.
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Hahsler, Michael, Kurt Hornik, and Christian Buchta. "Getting Things in Order: An Introduction to the R Package seriation." American Statistical Association, 2008. http://epub.wu.ac.at/4003/1/things.pdf.

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Seriation, i.e., finding a suitable linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides an infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.
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Feinerer, Ingo, Christian Buchta, Wilhelm Geiger, Johannes Rauch, Patrick Mair, and Kurt Hornik. "The textcat Package for n-Gram Based Text Categorization in R." American Statistical Association, 2013. http://epub.wu.ac.at/3985/1/textcat.pdf.

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Анотація:
Identifying the language used will typically be the first step in most natural language processing tasks. Among the wide variety of language identification methods discussed in the literature, the ones employing the Cavnar and Trenkle (1994) approach to text categorization based on character n-gram frequencies have been particularly successful. This paper presents the R extension package textcat for n-gram based text categorization which implements both the Cavnar and Trenkle approach as well as a reduced n-gram approach designed to remove redundancies of the original approach. A multi-lingual corpus obtained from the Wikipedia pages available on a selection of topics is used to illustrate the functionality of the package and the performance of the provided language identification methods. (authors' abstract)
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Книги з теми "Package R"

1

Corporation, Pension Benefit Guaranty, ed. 1994-R revised premium payment package. [Philadelphia, PA] (P.O. Box 7247-7426, Philadelphia 19170-7426): The Corporation, 1994.

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2

García Martínez, Constantino Antonio, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, and Arturo José Méndez Penín. Heart Rate Variability Analysis with the R package RHRV. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65355-6.

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3

Baio, Gianluca, Andrea Berardi, and Anna Heath. Bayesian Cost-Effectiveness Analysis with the R package BCEA. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55718-2.

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García Martínez, Constantino Antonio, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, and Arturo José Méndez Penín. Heart Rate Variability Analysis with the R package RHRV. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-65753-5.

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5

V, Metcalfe Andrew, ed. Introductory time series with R. Dordrecht: Springer, 2009.

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6

Cowpertwait, Paul S. P. Introductory time series with R. Dordrecht: Springer, 2009.

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7

Cowpertwait, Paul S. P. Introductory time series with R. Dordrecht: Springer, 2009.

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8

Spiros, Triantafyllopoulos, and United States. National Aeronautics and Space Administration., eds. General specifications for the development of a USL NASA PC R&D statistical analysis support package. Lafayette, La: University of Southwestern Louisiana, Computer Science Dept., 1989.

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9

Pfaff, Bernhard. Analysis of integrated and cointegrated time series with R. 2nd ed. New York: Springer, 2008.

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10

Indonesia. Direktorat Jenderal Penyiapan Pemukiman. Direktorat Bina Program. and BIEC International Inc, eds. Phase IIIA, final report, Sukaraja WPP XIVa SKP E (R): Phase IIIA studies of transmigration settlements development in West Kalimantan (package C). [Jakarta?]: China Engineering Consultants in association with BIEC International, 1987.

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Частини книг з теми "Package R"

1

Iacus, Stefano M., and Nakahiro Yoshida. "The YUIMA Package." In Use R!, 3–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-55569-0_1.

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2

Wiley, Matt, and Joshua F. Wiley. "Writing a Package." In Advanced R, 83–113. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2077-1_6.

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3

Debelak, Rudolf, Carolin Strobl, and Matthew D. Zeigenfuse. "R Package mirt." In An Introduction to the Rasch Model with Examples in R, 147–64. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315200620-7.

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Debelak, Rudolf, Carolin Strobl, and Matthew D. Zeigenfuse. "R Package TAM." In An Introduction to the Rasch Model with Examples in R, 165–72. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315200620-8.

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Debelak, Rudolf, Carolin Strobl, and Matthew D. Zeigenfuse. "R Package eRm." In An Introduction to the Rasch Model with Examples in R, 111–46. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315200620-6.

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6

Duşa, Adrian. "The QCA Package." In QCA with R, 19–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75668-4_2.

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Pace, Larry. "Making an R Package." In Beginning R, 269–88. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4555-1_19.

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Pace, Larry. "The R Commander Package." In Beginning R, 289–302. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4555-1_20.

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Principal components analysis." In Practical R for biologists: an introduction, 194–99. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0017.

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Abstract This chapter focuses on how to conduct a principal components analysis. To conduct principal components analysis, R has two similar built-in functions prcomp and princomp in the default stats package. Other implementations can be found in various downloadable packages, e.g. the function PCA from the package FactoMineR, the function dudi.pca from the package ade4 and the function acp from the package amap. The functions prcomp and princomp employ different calculation methods but in practice the results they return will be almost identical.
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Principal components analysis." In Practical R for biologists: an introduction, 194–99. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0194.

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Abstract This chapter focuses on how to conduct a principal components analysis. To conduct principal components analysis, R has two similar built-in functions prcomp and princomp in the default stats package. Other implementations can be found in various downloadable packages, e.g. the function PCA from the package FactoMineR, the function dudi.pca from the package ade4 and the function acp from the package amap. The functions prcomp and princomp employ different calculation methods but in practice the results they return will be almost identical.
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Тези доповідей конференцій з теми "Package R"

1

Lawson, Barry, and Lawrence M. Leemis. "An R package for simulation education." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8248124.

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2

Badea, Iulia, and Stefan Trausan-Matu. "CSCL chats analysis using R package." In 2014 18th International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2014. http://dx.doi.org/10.1109/icstcc.2014.6982530.

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3

Huang, Jie, Bojin Zhuang, and Fei Su. "A General Accelerated R Package Using GPU." In 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity). IEEE, 2015. http://dx.doi.org/10.1109/smartcity.2015.136.

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Priyavrat and Nonita Sharma. "Sentiment Analysis using tidytext package in R." In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). IEEE, 2018. http://dx.doi.org/10.1109/icsccc.2018.8703296.

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Vila, X. A., M. J. Lado, A. J. Mendez, D. N. Olivieri, and L. Rodriguez Linares. "An R package for heart rate variability analysis." In 2009 IEEE International Symposium on Intelligent Signal Processing - (WISP 2009). IEEE, 2009. http://dx.doi.org/10.1109/wisp.2009.5286528.

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Conde-Clemente, Patricia, Jose M. Alonso, and Gracian Trivino. "rLDCP: R package for text generation from data." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015487.

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Phaphan, Wikanda, Thanee Ananitthi, Ibrahim Abdullahi, and Sthaporn Thepsumritporn. "MGGD: R Package for Mixture Generalized Gamma Distribution." In 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C). IEEE, 2022. http://dx.doi.org/10.1109/ri2c56397.2022.9910325.

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Winser, M. "A single package X-Band T/R module." In 2005 European Microwave Conference. IEEE, 2005. http://dx.doi.org/10.1109/eumc.2005.1608901.

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Rumyantsev, Alexander. "Simulating Supercomputer Workload with Hpcwld Package for R." In 2014 15th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 2014. http://dx.doi.org/10.1109/pdcat.2014.36.

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Ordoñez Cuastumal, José Alejandro, Christian Eduardo Galarza Morales, and Víctor Hugo Víctor Hugo. "An R package for censored spatial data analysis." In VIII Congreso Internacional de Investigación REDU. Medwave, 2022. http://dx.doi.org/10.5867/medwave.2022.s1.ci54.

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Звіти організацій з теми "Package R"

1

Castleton, Karl J., Gene Whelan, and Bonnie L. Hoopes. FRAMES Software System: Linking to the Statistical Package R. Office of Scientific and Technical Information (OSTI), December 2006. http://dx.doi.org/10.2172/944521.

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2

Gu, Jiaying, and Roger Koenker. Rebayes: an R package for empirical bayes mixture methods. The IFS, August 2017. http://dx.doi.org/10.1920/wp.cem.2017.3717.

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3

Silerio-Vazquez, Martin. SynMRI: An R Package for Synthetic Magnetic Resonance Imaging. Ames (Iowa): Iowa State University, January 2020. http://dx.doi.org/10.31274/cc-20240624-1369.

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4

Nash, Edward John, Aiva Jasilioniene, and Evgueni M. Andreev. An "R" package for the production of cohort fertility tables. Rostock: Max Planck Institute for Demographic Research, November 2010. http://dx.doi.org/10.4054/mpidr-tr-2010-007.

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5

Fernandez-Val, Ivan, Michael Lipsitz, Alexandre Belloni, and Victor Chernozhukov. Quantreg.nonpar: an R package for performing nonparametric series quantile regression. The IFS, June 2017. http://dx.doi.org/10.1920/wp.cem.2017.2917.

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6

Dańko, Maciej J. Analysis of reporting behavior using the hopit R-package (v0.11.5). Rostock: Max Planck Institute for Demographic Research, October 2022. http://dx.doi.org/10.4054/mpidr-tr-2023-001.

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7

Zyphur, Michael. Instrumental Variable Analysis Using lavaan in R. Instats Inc., 2022. http://dx.doi.org/10.61700/ff0rzg91cv2ku469.

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Анотація:
This seminar introduces the lavaan package in R for estimating causal effects with observational data using instrumental variables in both path analysis and SEM, grounded in introductions to both techniques including CFA. Traditional bootstrapping and Bayesian methods are explored for estimation and hypothesis testing. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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Cortez, Paulo. A tutorial on using the rminer R package for data mining tasks. University of Minho, July 2015. http://dx.doi.org/10.21814/1822.36210.

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9

Cheung, Mike. Meta-Analytic SEM in R. Instats Inc., 2023. http://dx.doi.org/10.61700/2sgaqfuzkt040469.

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Анотація:
This seminar introduces the logic of meta-analytic structural equation modeling (MASEM) and illustrates how to conduct the analyses with R. Meta-analytic SEM is an incredibly powerful tool for hypothesis and theory testing, relying on pooled correlation matrices from primary studies, and this seminar will teach you the basics of MASEM and how to apply it in your own research, using many hands-on examples with Professor Cheung's R package for MASEM. When purchasing the MASEM seminar you will be freely enrolled in two on-demand seminars that introduce the logic of path analysis and CFA/SEM in R by Professor Zyphur, offering a substantial value. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Jdanov, Dmitri A., and Edward John Nash. An "R" package for the production of a Lexis database of fertility data. Rostock: Max Planck Institute for Demographic Research, May 2011. http://dx.doi.org/10.4054/mpidr-tr-2011-003.

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