Relevant bibliographies by topics / An R package

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'An R package'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "An R package"

1

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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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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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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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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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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Dissertations / Theses on the topic "An R package"

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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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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 repo
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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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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-autho
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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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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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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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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)<br>Series: Research Report S
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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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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, a
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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
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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
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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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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-l
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Books on the topic "An R package"

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García Martínez, Constantino Antonio, Abraham Otero Quintana, Xosé A. Vila, et al. Heart Rate Variability Analysis with the R package RHRV. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65355-6.

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

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Inc, BIEC International, and Indonesia. Direktorat Bina Program Penyiapan Pemukiman., eds. Phase IIIA, final report, Sukaraja WPP XIVa SKP D1 (R): Phase IIIA studies of transmigration settlements development in West Kalimantan (package C). China Engineering Consultants, Inc. in association with BIEC International Inc., 1988.

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Reconnaissance and outline planning and preparation of SP structure plans for transm[i]g[r]ation settlements development Irian Jaya (package J), phase II, final report: SKP Bupul E, WPP XXIII b. Republic of Indonesia, Departemen Transmigrasi, Direktorat Jenderal Penyiapan Pemukiman, Direktorat Bina Program, 1987.

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Hölzer, Michael. Quality management with SAP R/3: Making it work for your business. SAP Press/Addison-Wesley, 2001.

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Teufel, T. SAP Business one. Thomson/Course Technology, 2005.

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Tim, Rhodes, and Davis Jeff, eds. Sams teach yourself SAP in 24 hours. Sam Pub., 2008.

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Danielle, Larocca, ed. Sams teach yourself SAP in 24 hours. 2nd ed. Sams, 2006.

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Corporation, Pension Benefit Guaranty, ed. 1994-R revised premium payment package. The Corporation, 1994.

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1994-R revised premium payment package. The Corporation, 1994.

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Book chapters on the topic "An R package"

1

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

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

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Duşa, Adrian. "The QCA Package." In QCA with R. 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. 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. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4555-1_20.

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Kelley, Dan E. "The oce Package." In Oceanographic Analysis with R. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8844-0_3.

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Principal components analysis." In Practical R for biologists: an introduction. 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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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Principal components analysis." In Practical R for biologists: an introduction. 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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Mailund, Thomas. "Building an R Package." In Beginning Data Science in R. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2671-1_11.

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Quicke, Donald L. J., Buntika A. Butcher, and Rachel A. Kruft Welton. "Phylogenies and trees." In Practical R for biologists: an introduction. CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0275.

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Abstract Several packages have been developed to allow R-users to work with phylogenetic trees, something that most biologists will need to do at some point in their careers. The most basic is the ape package, which stands for Analysis of Phylogenetics and Evolution. This chapter gives some of the basics of handling 'trees' in R and show things that can be calculated with them. Phytools, another package with extra capabilities, are also introduced in this chapter. Insects are given as examples.
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Conference papers on the topic "An R package"

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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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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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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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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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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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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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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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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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Tsiliki, Georgia, Cristian Munteanu, Jose Seoane, Carlos Fernandez-Lozano, Haralambos Sarimveis, and Egon Willighagen. "Using the RRegrs R package for automating predictive modelling." In MOL2NET, International Conference on Multidisciplinary Sciences. MDPI, 2015. http://dx.doi.org/10.3390/mol2net-1-f009.

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Deng, Yue, and Lin Gao. "ppiPre - an R package for predicting protein-protein interactions." In 2012 IEEE 6th International Conference on Systems Biology (ISB). IEEE, 2012. http://dx.doi.org/10.1109/isb.2012.6314159.

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Reports on the topic "An R package"

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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), 2006. http://dx.doi.org/10.2172/944521.

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

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

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

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Cortez, Paulo. A tutorial on using the rminer R package for data mining tasks. University of Minho, 2015. http://dx.doi.org/10.21814/1822.36210.

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Jdanov, Dmitri A., and Edward John Nash. An "R" package for the production of a Lexis database of fertility data. Max Planck Institute for Demographic Research, 2011. http://dx.doi.org/10.4054/mpidr-tr-2011-003.

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Spindler, Martin, Victor Chernozhukov, and Philipp Bach. Valid simultaneous inference in high-dimensional settings (with the HDM package for R). The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.3019.

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Fraley, Chris, and Adrian E. Raftery. MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada456562.

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Reynolds, R. M., and S. Smith. The R/V Discoverer cruise to Manus Island. The BNL Portable Radiometer Package (PRP) evaluation. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/481570.

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Elatar, Ahmed, Ahmad Abuheiba, Viral K. Patel, et al. Risk Assessment of Catastrophic Leak Of R-452b From Package Unit into A Residential Space. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1606923.

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