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  "Title": "Robust Markov Chain Monte Carlo Methods",
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  "Description": "Functions for simulating Markov chains to compute Markov\nchain Monte Carlo (MCMC) estimates of expectations with respect\nto a target distribution on a real-valued vector space. The\npackage has built-in support for using Metropolis-Hastings\ntransitions with random-walk, Langevin, Hamiltonian or Barker\nproposals, and is designed to be easy to extend with additional\nalgorithms. Proposal shape and scale parameters can be\nautomatically adapted in an initial warm-up stage. The package\nhas a modular design allowing different algorithmic components\nsuch as proposals and adapters to be mixed and matched. If\n'BridgeStan's R interface\n<https://roualdes.us/bridgestan/latest/languages/r.html>,\navailable on GitHub <https://github.com/roualdes/bridgestan>,\nis installed, then it can be used to specify the target\ndistribution to sample from.",
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      "topics": [
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      ]
    },
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      "title": "Create a new Barker proposal object with bimodal noise distribution.",
      "topics": [
        "bimodal_barker_proposal"
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      "title": "Construct a new chain state.",
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    },
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      "title": "Create object to adapt proposal with shape based on estimate of target distribution covariance matrix.",
      "topics": [
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      ]
    },
    {
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      "topics": [
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      ]
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      "topics": [
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      "topics": [
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      "title": "Create object to adapt proposal shape (and scale) using robust adaptive Metropolis algorithm of Vihola (2012).",
      "topics": [
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      "title": "Create object to adapt proposal scale to coerce average acceptance rate.",
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