[ 原始碼： r-bioc-s4vectors ]
套件： r-bioc-s4vectors (0.24.3-1)
r-bioc-s4vectors 的相關超連結
Trisquel 的資源：
下載原始碼套件 r-bioc-s4vectors：
- [r-bioc-s4vectors_0.24.3-1.dsc]
- [r-bioc-s4vectors_0.24.3.orig.tar.gz]
- [r-bioc-s4vectors_0.24.3-1.debian.tar.xz]
維護者：
Original Maintainers:
- Debian R Packages Maintainers
- Andreas Tille
外部的資源：
- 主頁 [bioconductor.org]
相似套件：
BioConductor S4 implementation of vectors and lists
The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).
其他與 r-bioc-s4vectors 有關的套件
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- dep: libc6 (>= 2.14) [amd64]
- GNU C Library: Shared libraries
同時作為一個虛擬套件由這些套件提供： libc6-udeb
- dep: libc6 (>= 2.7) [armhf]
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- dep: r-api-3.5
- 本虛擬套件由這些套件提供： r-base-core
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- dep: r-api-bioc-3.10
- 本虛擬套件由這些套件提供： r-bioc-biocgenerics
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- dep: r-base-core (>= 3.6.2-2)
- GNU R core of statistical computation and graphics system
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- dep: r-bioc-biocgenerics (>= 0.31.1)
- generic functions for Bioconductor
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- rec: r-bioc-genomicranges
- BioConductor representation and manipulation of genomic intervals
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- rec: r-bioc-iranges (>= 2.0.0)
- GNU R low-level containers for storing sets of integer ranges
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- rec: r-cran-runit
- GNU R package providing unit testing framework
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- sug: r-bioc-delayedarray
- BioConductor delayed operations on array-like objects
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- sug: r-bioc-graph
- handle graph data structures for BioConductor
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- sug: r-bioc-shortread
- GNU R classes and methods for high-throughput short-read sequencing data
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- sug: r-bioc-summarizedexperiment
- BioConductor assay container
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- sug: r-cran-data.table
- GNU R extension of Data.frame
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- sug: r-cran-matrix
- GNU R package of classes for dense and sparse matrices