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  "Title": "Particle Learning of Gaussian Processes",
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  "Description": "Sequential Monte Carlo (SMC) inference for fully Bayesian\nGaussian process (GP) regression and classification models by\nparticle learning (PL) following Gramacy & Polson (2011)\n<doi:10.48550/arXiv.0909.5262>. The sequential nature of\ninference and the active learning (AL) hooks provided\nfacilitate thrifty sequential design (by entropy) and\noptimization (by improvement) for classification and regression\nmodels, respectively. This package essentially provides a\ngeneric PL interface, and functions (arguments to the\ninterface) which implement the GP models and AL heuristics.\nFunctions for a special, linked, regression/classification GP\nmodel and an integrated expected conditional improvement (IECI)\nstatistic provide for optimization in the presence of unknown\nconstraints. Separable and isotropic Gaussian, and single-index\ncorrelation functions are supported. See the examples section\nof ?plgp and demo(package=\"plgp\") for an index of demos.",
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    "getmap.CGP",
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    "init.GP",
    "latents.CGP",
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    "prior.ConstGP",
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      "title": "Particle Learning of Gaussian Processes",
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        "addpall.GP"
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        "data.CGP.adapt",
        "data.ConstGP",
        "data.ConstGP.improv",
        "data.GP",
        "data.GP.improv"
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        "draw.GP"
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