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R-GenomicAlignments-1.28.0-3.fc36.x86_64
Provides efficient containers for storing and manipulating short genomic
alignments (typically obtained by aligning short reads to a reference genome).
This includes read counting, computing the coverage, junction detection, and
working with the nucleotide content of the alignments.
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R-GenomicRanges-1.44.0-3.fc36.x86_64
The ability to efficiently store genomic annotations and alignments is
playing a central role when it comes to analyze high-throughput sequencing
data (a.k.a. NGS data). The package defines general purpose containers for
storing genomic intervals as well as more specialized containers for
storing alignments against a reference genome.
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R-IRanges-2.26.0-3.fc36.x86_64
The IRanges class and its extensions are low-level containers
for storing sets of integer ranges. A typical use of these containers
in biology is for representing a set of chromosome regions.
More specific extensions of the IRanges class will typically
allow the storage of additional information attached to each
chromosome region as well as a hierarchical relationship between
these regions.
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R-IRdisplay-1.0-4.fc36.noarch
An interface to the rich display capabilities of 'Jupyter' front-ends
(e.g. 'Jupyter Notebook') <https://jupyter.org>. Designed to be used from a
running 'IRkernel' session <https://irkernel.github.io>.
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R-IRkernel-1.2-3.fc36.noarch
The R kernel for the 'Jupyter' environment executes R code which the front-end
('Jupyter Notebook' or other front-ends) submits to the kernel via the network.
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R-R.cache-0.15.0-3.fc36.noarch
Memoization can be used to speed up repetitive and computational expensive
function calls. The first time a function that implements memoization is
called the results are stored in a cache memory. The next time the
function is called with the same set of parameters, the results are
momentarily retrieved from the cache avoiding repeating the calculations.
With this package, any R object can be cached in a key-value storage where
the key can be an arbitrary set of R objects. The cache memory is
persistent (on the file system).
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R-R.devices-2.17.0-4.fc36.noarch
Functions for creating plots and image files in a unified way regardless of
output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default device options
as well as scales and aspect ratios are controlled in a uniform way across
all device types. Switching output format requires minimal changes in code.
This package is ideal for large-scale batch processing, because it will
never leave open graphics devices or incomplete image files behind, even on
errors or user interrupts.
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R-R.methodsS3-1.8.1-5.fc36.noarch
Methods that simplify the setup of S3 generic functions and S3 methods. Major
effort has been made in making definition of methods as simple as possible with
a minimum of maintenance for package developers. For example, generic
functions are created automatically, if missing, and naming conflict are
automatically solved, if possible. The method setMethodS3() is a good start
for those who in the future may want to migrate to S4. This is a
cross-platform package implemented in pure R that generates standard S3
methods.
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R-R.oo-1.24.0-5.fc36.noarch
Methods and classes for object-oriented programming in R with or without
references. Large effort has been made on making definition of methods as
simple as possible with a minimum of maintenance for package developers.
The package has been developed since 2001 and is now considered very
stable. This is a cross-platform package implemented in pure R that
defines standard S3 classes without any tricks.
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R-R.rsp-0.44.0-5.fc36.noarch
The RSP markup language makes any text-based document come alive. RSP provides
a powerful markup for controlling the content and output of LaTeX, HTML,
Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date
is <%=Sys.Date()%>'. Contrary to many other literate programming languages,
with RSP it is straightforward to loop over mixtures of code and text sections,
e.g. in month-by-month summaries. RSP has also several preprocessing
directives for incorporating static and dynamic contents of external files
(local or online) among other things. Functions rstring() and rcat() make it
easy to process RSP strings, rsource() sources an RSP file as it was an R
script, while rfile() compiles it (even online) into its final output format,
e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp')
generates 'report.html'. RSP is ideal for self-contained scientific reports
and R package vignettes. It's easy to use - if you know how to write an R
script, you'll be up and running within minutes.
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