How to Install and Uninstall r-cran-mcmcpack Package on Kali Linux

Last updated: May 22,2024

1. Install "r-cran-mcmcpack" package

Here is a brief guide to show you how to install r-cran-mcmcpack on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-mcmcpack
            
                    
    
    Copied

2. Uninstall "r-cran-mcmcpack" package

This tutorial shows how to uninstall r-cran-mcmcpack on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-cran-mcmcpack
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-mcmcpack package on Kali Linux

Package: r-cran-mcmcpack
Version: 1.7-0-1
Installed-Size: 3747
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-api-4.0, r-cran-coda (>= 0.11-3), r-cran-mass, r-cran-lattice, r-cran-mcmc, r-cran-quantreg, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1)
Size: 1872404
SHA256: 9be265d33c5b87bc45c08ecb4ee752ce2ec86dee9a91f0c590ffa07217dd2f11
SHA1: 30c6d8c8fab166ba4b7d9fd8b461d5f56786463f
MD5sum: ecda193b300094795408a76d6ea66c9b
Description: R routines for Markov chain Monte Carlo model estimation
This is a set of routines for GNU R that implement various
statistical and econometric models using Markov chain Monte Carlo
(MCMC) estimation, which allows "solving" models that would otherwise
be intractable with traditional techniques, particularly problems in
Bayesian statistics (where one or more "priors" are used as part of
the estimation procedure, instead of an assumption of ignorance about
the "true" point estimates), although MCMC can also be used to solve
frequentist statistical problems with uninformative priors. MCMC
techniques are also preferable over direct estimation in the presence
of missing data.
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Currently implemented are a number of ecological inference (EI)
routines (for estimating individual-level attributes or behavior from
aggregate data, such as electoral returns or census results), as well
as models for traditional linear panel and cross-sectional data, some
visualization routines for EI diagnostics, two item-response theory
(or ideal-point estimation) models, metric, ordinal, and
mixed-response factor analysis, and models for Gaussian (linear) and
Poisson regression, logistic regression (or logit), and binary and
ordinal-response probit models.
.
The suggested packages (r-cran-bayesm, -eco, and -mnp) contain
additional models that may also be useful for those interested in
this package.
Description-md5:
Homepage: https://cran.r-project.org/package=MCMCpack
Tag: devel::lang:r, devel::library, field::statistics, implemented-in::r,
role::app-data, suite::gnu
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-mcmcpack/r-cran-mcmcpack_1.7-0-1_amd64.deb