How to Install and Uninstall r-cran-tgp Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: November 22,2024

1. Install "r-cran-tgp" package

Please follow the instructions below to install r-cran-tgp on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This guide covers the steps necessary to uninstall r-cran-tgp on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-tgp
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-tgp package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-tgp
Architecture: amd64
Version: 2.4-14-4build2
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 2781
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-maptree, libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2)
Suggests: r-cran-mass
Filename: pool/universe/r/r-cran-tgp/r-cran-tgp_2.4-14-4build2_amd64.deb
Size: 2424688
MD5sum: f173ee1423491ab8200e2c86510c4341
SHA1: f75dda64676a98dcacc1db9b6dbe8fe6939f09e2
SHA256: 67e5425d2e47704b4a38d52e79dd61cf0197b90839343ce0d679133ed1c7d2be
SHA512: cc0036ab7e1d659a21ecfddfe40116f1884bbb3ad293d27d0f564bfb14821788ebf15233dadc6aa9137310ac8f0f02e421d1b842eb19820ad2d10d9bc1f73b5e
Homepage: https://cran.r-project.org/package=tgp
Description-en: GNU R Bayesian treed Gaussian process models
Bayesian nonstationary, semiparametric nonlinear regression and design by
treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM).
Special cases also implemented include Bayesian linear models, CART, treed
linear models, stationary separable and isotropic GPs, and GP single-index
models. Provides 1-d and 2-d plotting functions (with projection and slice
capabilities) and tree drawing, designed for visualization of tgp-class
output. Sensitivity analysis and multi-resolution models are supported.
Sequential experimental design and adaptive sampling functions are also
provided, including ALM, ALC, and expected improvement. The latter supports
derivative-free optimization of noisy black-box functions.
Description-md5: 8df682c19562241dc98fcd8ada74723c