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  "Title": "Parallel Chain Tools for Bayesian Kernel Machine Regression",
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  "Authors@R": "person(\"Alexander\", \"Keil\", email=\"alex.keil@nih.gov\", role = c(\"aut\", \"cre\"))",
  "Description": "Bayesian kernel machine regression (from the 'bkmr'\npackage) is a Bayesian semi-parametric generalized linear model\napproach under identity and probit links. There are a number of\nfunctions in this package that extend Bayesian kernel machine\nregression fits to allow multiple-chain inference and\ndiagnostics, which leverage functions from the 'future',\n'rstan', and 'coda' packages.  Reference: Bobb, J. F., Henn, B.\nC., Valeri, L., & Coull, B. A. (2018). Statistical software for\nanalyzing the health effects of multiple concurrent exposures\nvia Bayesian kernel machine regression. ;\n<doi:10.1186/s12940-018-0413-y>.",
  "License": "GPL (>= 3)",
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  "Date/Publication": "2025-11-24 18:35:51 UTC",
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    "kmbayes_parallel_continue",
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    "PredictorResponseBivar_parallel",
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    "SamplePred_parallel",
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    },
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      "page": "as.mcmc.list.bkmrfit.list",
      "title": "Convert multi-chain bkmrfit to mcmc.list for coda MCMC diagnostics",
      "topics": [
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    },
    {
      "page": "ExtractPIPs_parallel",
      "title": "Posterior inclusion probabilities by chain",
      "topics": [
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        "kmbayes_combine"
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        "kmbayes_diagnose"
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