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

Last updated: May 09,2024

1. Install "r-cran-tgp" package

This is a short guide on how to install r-cran-tgp on Ubuntu 21.10 (Impish Indri)


    
        
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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

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


    
        
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                sudo apt remove
            
                        
                r-cran-tgp
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-tgp package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-tgp
Architecture: amd64
Version: 2.4-17-1
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: 3602
Depends: r-base-core (>= 4.0.2-1build1), 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_2.4-17-1_amd64.deb
Size: 3077116
MD5sum: 09eb432446cf8fadc6aa7973ba4b6b4e
SHA1: 0903e3e2a835e22d896d4d6e6d97c500c98da4c6
SHA256: 940cebe22ab5802f491c8d9143cec58af0e15aff90e7caa2207a690fac5ed5c6
SHA512: 7b056033f36d86643cc3383b8a0530d6c9d1c604540950bff61389a1d604872154f0228d427b8d101fa6b8d95f5173d7b26cc1ff6aaf0a154cff9ac24426b43c
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