How to Install and Uninstall r-cran-mcmcpack Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: May 14,2024

1. Install "r-cran-mcmcpack" package

Please follow the steps below to install r-cran-mcmcpack on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-mcmcpack
            
                    
    
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2. Uninstall "r-cran-mcmcpack" package

In this section, we are going to explain the necessary steps to uninstall r-cran-mcmcpack on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
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                sudo apt remove
            
                        
                r-cran-mcmcpack
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-mcmcpack package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: r-cran-mcmcpack
Priority: optional
Section: universe/math
Installed-Size: 2811
Maintainer: Ubuntu Developers
Original-Maintainer: Chris Lawrence
Architecture: amd64
Source: mcmcpack
Version: 1.3-3-1
Depends: libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.6), r-cran-coda (>= 0.16), r-cran-mass, r-base-core (>= 3.0.0-2ubuntu1)
Suggests: r-cran-bayesm, r-cran-eco, r-cran-mnp
Filename: pool/universe/m/mcmcpack/r-cran-mcmcpack_1.3-3-1_amd64.deb
Size: 1466048
MD5sum: 9a9b69bd2e7df427cc1c293a36798607
SHA1: 04e227a5f3bc1830f77fbda805f04e7133b97e6d
SHA256: 1a5adfbdae5a03253bf39c842d2bad9581a5ac74095e0e63484e3a075828cf74
Description-en: 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: e61e7c97144ccf110c561d9a0afdc130
Homepage: http://mcmcpack.wustl.edu/
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu