How to Install and Uninstall r-cran-msm Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 17,2024

1. Install "r-cran-msm" package

This tutorial shows how to install r-cran-msm on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

Please follow the steps below to uninstall r-cran-msm on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-cran-msm
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-msm package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-msm
Architecture: amd64
Version: 1.6.8-1build1
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1632
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-survival, r-cran-mvtnorm, r-cran-expm, libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3
Recommends: r-cran-numderiv, r-cran-testthat
Suggests: r-cran-minqa, r-cran-doparallel, r-cran-foreach
Filename: pool/universe/r/r-cran-msm/r-cran-msm_1.6.8-1build1_amd64.deb
Size: 1446936
MD5sum: 0e9fc584b76055322404c2841603e101
SHA1: f85f2933a1d8f28102b56a2783022dc8e666a0cb
SHA256: e3d290098491b95302eb2d6dc9cbbb20008effc9c607eead7c57b1d0a145f922
SHA512: 264bf87cbaf2c3918d19aa239174c1d2118a1b3dc237e4854fd8c3e80aff4b82cc609fb11bd8e02fbd97723dd0057cdb7e5b4fc11e10578f05f16b4e65ad0d76
Homepage: https://cran.r-project.org/package=msm
Description-en: GNU R Multi-state Markov and hidden Markov models in continuous time
Functions for fitting general continuous-time Markov and hidden Markov
multi-state models to longitudinal data. Both Markov transition rates and the
hidden Markov output process can be modelled in terms of covariates. A variety
of observation schemes are supported, including processes observed at arbitrary
times, completely-observed processes, and censored states.
Description-md5: d56de45b477deb65af5ef08165aeef03