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Everything about R package
Extensions to the R statistical programming language
R packages are extensions to the R statistical programming language. R packages contain code, data, and documentation in a standardised collection format that can be installed by users of R, typically via a centralised software repository such as CRAN. The large number of packages available for R, and the ease of installing and using them, has been cited as a major factor driving the widespread adoption of the language in data science. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: R packageA 5-minute orientation
- Skim a recent paper: The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*Influential work since 2022
- Pick up a classic book: Introductory time series with RThe most-reprinted book on the subject
- Read the landmark paper: edgeR : a Bioconductor package for differential expression analysis of digital gene expression dataCited 41,337 times
- Go sideways: R (programming language)A closely related topic
- Go sideways: Rnn (software)A closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
edgeR : a Bioconductor package for differential expression analysis of digital gene expression data
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
WGCNA: an R package for weighted correlation network analysis
lavaan : An R Package for Structural Equation Modeling
lmerTest Package: Tests in Linear Mixed Effects Models
phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data
Conducting Meta-Analyses in R with the metafor Package
Astropy: A community Python package for astronomy
🆕 Influential since 2022
The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*
PRISMA2020 : An R package and Shiny app for producing PRISMA 2020‐compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis
GSEApy: a comprehensive package for performing gene set enrichment analysis in Python
Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package
ggtranscript : an R package for the visualization and interpretation of transcript isoforms using ggplot2
📚 Books worth reading
Introductory time series with RPaul S. P. Cowpertwait, Andrew V. Metcalfe · 2009
Sams teach yourself SAP in 24 hoursGeorge W. Anderson, Anderson, George · 2005
Design of Analog CMOS Integrated CircuitsBehzad Razavi · 2000
Analysis of integrated and cointegrated time series with RBernhard Pfaff · 2006
SAP Business oneT. Teufel, Thomas Teufel · 2005
Quality management with SAP R/3Michael Hölzer, Michael Holzer · 2000
The packaging designer's book of patternsLazlo Roth, George L. Wybenga · 2000
Sous-Vide CuisineJoan Roca · 2005
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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