How to Install and Uninstall R-future.noarch Package on Fedora 34

Last updated: November 16,2024

1. Install "R-future.noarch" package

Here is a brief guide to show you how to install R-future.noarch on Fedora 34


    
        
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                sudo dnf update
            
                    
    
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            $
        

        
            
                sudo dnf install
            
                        
                R-future.noarch
            
                    
    
    Copied

2. Uninstall "R-future.noarch" package

This guide let you learn how to uninstall R-future.noarch on Fedora 34:


    
        
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                sudo dnf remove
            
                        
                R-future.noarch
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the R-future.noarch package on Fedora 34

Last metadata expiration check: 0:17:54 ago on Tue Sep 6 02:10:55 2022.
Available Packages
Name : R-future
Version : 1.21.0
Release : 2.fc34
Architecture : noarch
Size : 582 k
Source : R-future-1.21.0-2.fc34.src.rpm
Repository : fedora
Summary : Unified Parallel and Distributed Processing in R for Everyone
URL : https://CRAN.R-project.org/package=future
License : LGPLv2+
Description : The purpose of this package is to provide a lightweight and unified Future API
: for sequential and parallel processing of R expression via futures. The
: simplest way to evaluate an expression in parallel is to use `x %<-% {
: expression }` with `plan(multiprocess)`. This package implements sequential,
: multicore, multisession, and cluster futures. With these, R expressions can be
: evaluated on the local machine, in parallel a set of local machines, or
: distributed on a mix of local and remote machines. Extensions to this package
: implement additional backends for processing futures via compute cluster
: schedulers etc. Because of its unified API, there is no need to modify any code
: in order switch from sequential on the local machine to, say, distributed
: processing on a remote compute cluster. Another strength of this package is
: that global variables and functions are automatically identified and exported
: as needed, making it straightforward to tweak existing code to make use of
: futures.