{
  "_id": "6a1d55f71d7bb097a0a46e62",
  "Package": "BayesFluxR",
  "Type": "Package",
  "Title": "Implementation of Bayesian Neural Networks",
  "Version": "0.1.3",
  "Authors@R": "c(person(given=\"Enrico\",\nfamily=\"Wegner\",\nrole=c(\"aut\", \"cre\"),\nemail=\"e.wegner@student.maastrichtuniversity.nl\"))",
  "Maintainer": "Enrico Wegner <e.wegner@student.maastrichtuniversity.nl>",
  "Description": "Implementation of 'BayesFlux.jl' for R; It extends the\nfamous 'Flux.jl' machine learning library to Bayesian Neural\nNetworks. The goal is not to have the fastest production ready\nlibrary, but rather to allow more people to be able to use and\nresearch on Bayesian Neural Networks.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.2.3",
  "Config/testthat/edition": "3",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-01 09:48:06 UTC",
    "User": "root"
  },
  "Author": "Enrico Wegner [aut, cre]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2023-12-11 02:39:01 UTC",
  "RemoteUrl": "https://github.com/cran/BayesFluxR",
  "RemoteRef": "HEAD",
  "RemoteSha": "a0de5620ed1281167590875800d95fd16ce1440e",
  "MD5sum": "3c5ef41ffb2604204b4b87ab35131100",
  "_user": "cran",
  "_type": "src",
  "_file": "BayesFluxR_0.1.3.tar.gz",
  "_fileid": "2374b4075c0a36d350bf5303268df9ccbbfe0118b0581bebdbf05bbeb4c579d3",
  "_filesize": 1249344,
  "_sha256": "2374b4075c0a36d350bf5303268df9ccbbfe0118b0581bebdbf05bbeb4c579d3",
  "_created": "2026-06-01T09:48:06.000Z",
  "_published": "2026-06-01T09:50:47.042Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 78826915770,
      "time": 123,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7328366349"
    },
    {
      "job": 78826915814,
      "time": 111,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7328362773"
    },
    {
      "job": 78826448281,
      "time": 170,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7328322691"
    },
    {
      "job": 78826915771,
      "time": 117,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7328364488"
    }
  ],
  "_buildurl": "https://github.com/r-universe/cran/actions/runs/26747409217",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/cran/BayesFluxR",
  "_commit": {
    "id": "a0de5620ed1281167590875800d95fd16ce1440e",
    "author": "Enrico Wegner <e.wegner@student.maastrichtuniversity.nl>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 0.1.3\n",
    "time": 1702262341
  },
  "_maintainer": {
    "name": "Enrico Wegner",
    "email": "e.wegner@student.maastrichtuniversity.nl"
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "JuliaCall",
      "version": ">= 0.17.5",
      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "testthat",
      "version": ">= 3.0.0",
      "role": "Suggests"
    }
  ],
  "_owner": "cran",
  "_selfowned": false,
  "_usedby": 0,
  "_updates": [],
  "_tags": [],
  "_stars": 0,
  "_userbio": {
    "uuid": 6899542,
    "type": "organization",
    "name": "cran",
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
    "count": 198,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/BayesFluxR"
  },
  "_searchresults": 4,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/BayesFluxR.html",
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
  ],
  "_realowner": "cran",
  "_cranurl": false,
  "_releases": [
    {
      "version": "0.1.1",
      "date": "2023-03-09"
    },
    {
      "version": "0.1.2",
      "date": "2023-10-06"
    },
    {
      "version": "0.1.3",
      "date": "2023-12-10"
    }
  ],
  "_exports": [
    ".set_seed",
    "bayes_by_backprop",
    "BayesFluxR_setup",
    "BNN",
    "BNN.totparams",
    "Chain",
    "Dense",
    "find_mode",
    "Gamma",
    "initialise.allsame",
    "InverseGamma",
    "likelihood.feedforward_normal",
    "likelihood.feedforward_tdist",
    "likelihood.seqtoone_normal",
    "likelihood.seqtoone_tdist",
    "LSTM",
    "madapter.DiagCov",
    "madapter.FixedMassMatrix",
    "madapter.FullCov",
    "madapter.RMSProp",
    "mcmc",
    "Normal",
    "opt.ADAM",
    "opt.Descent",
    "opt.RMSProp",
    "posterior_predictive",
    "prior_predictive",
    "prior.gaussian",
    "prior.mixturescale",
    "RNN",
    "sadapter.Const",
    "sadapter.DualAverage",
    "sampler.AdaptiveMH",
    "sampler.GGMC",
    "sampler.HMC",
    "sampler.SGLD",
    "sampler.SGNHTS",
    "tensor_embed_mat",
    "to_bayesplot",
    "Truncated",
    "vi.get_samples"
  ],
  "_help": [
    {
      "page": "dot-install_pkg",
      "title": "Installs Julia packages if needed",
      "topics": [
        ".install_pkg"
      ]
    },
    {
      "page": "dot-julia_project_status",
      "title": "Obtain the status of the current Julia project",
      "topics": [
        ".julia_project_status"
      ]
    },
    {
      "page": "dot-set_seed",
      "title": "Set a seed both in Julia and R",
      "topics": [
        ".set_seed"
      ]
    },
    {
      "page": "dot-using",
      "title": "Loads Julia packages",
      "topics": [
        ".using"
      ]
    },
    {
      "page": "bayes_by_backprop",
      "title": "Use Bayes By Backprop to find Variational Approximation to BNN.",
      "topics": [
        "bayes_by_backprop"
      ]
    },
    {
      "page": "BayesFluxR_setup",
      "title": "Set up of the Julia environment needed for BayesFlux",
      "topics": [
        "BayesFluxR_setup"
      ]
    },
    {
      "page": "BNN",
      "title": "Create a Bayesian Neural Network",
      "topics": [
        "BNN"
      ]
    },
    {
      "page": "BNN.totparams",
      "title": "Obtain the total parameters of the BNN",
      "topics": [
        "BNN.totparams"
      ]
    },
    {
      "page": "Chain",
      "title": "Chain various layers together to form a network",
      "topics": [
        "Chain"
      ]
    },
    {
      "page": "Dense",
      "title": "Create a Dense layer with `in_size` inputs and `out_size` outputs using `act` activation function",
      "topics": [
        "Dense"
      ]
    },
    {
      "page": "find_mode",
      "title": "Find the MAP of a BNN using SGD",
      "topics": [
        "find_mode"
      ]
    },
    {
      "page": "Gamma",
      "title": "Create a Gamma Prior",
      "topics": [
        "Gamma"
      ]
    },
    {
      "page": "get_random_symbol",
      "title": "Creates a random string that is used as variable in julia",
      "topics": [
        "get_random_symbol"
      ]
    },
    {
      "page": "initialise.allsame",
      "title": "Initialises all parameters of the network, all hyper parameters of the prior and all additional parameters of the likelihood by drawing random values from `dist`.",
      "topics": [
        "initialise.allsame"
      ]
    },
    {
      "page": "InverseGamma",
      "title": "Create an Inverse-Gamma Prior",
      "topics": [
        "InverseGamma"
      ]
    },
    {
      "page": "likelihood.feedforward_normal",
      "title": "Use a Normal likelihood for a Feedforward network",
      "topics": [
        "likelihood.feedforward_normal"
      ]
    },
    {
      "page": "likelihood.feedforward_tdist",
      "title": "Use a t-Distribution likelihood for a Feedforward network",
      "topics": [
        "likelihood.feedforward_tdist"
      ]
    },
    {
      "page": "likelihood.seqtoone_normal",
      "title": "Use a Normal likelihood for a seq-to-one recurrent network",
      "topics": [
        "likelihood.seqtoone_normal"
      ]
    },
    {
      "page": "likelihood.seqtoone_tdist",
      "title": "Use a T-likelihood for a seq-to-one recurrent network.",
      "topics": [
        "likelihood.seqtoone_tdist"
      ]
    },
    {
      "page": "LSTM",
      "title": "Create an LSTM layer with `in_size` input size, and `out_size` hidden state size",
      "topics": [
        "LSTM"
      ]
    },
    {
      "page": "madapter.DiagCov",
      "title": "Use the diagonal of sample covariance matrix as inverse mass matrix.",
      "topics": [
        "madapter.DiagCov"
      ]
    },
    {
      "page": "madapter.FixedMassMatrix",
      "title": "Use a fixed mass matrix",
      "topics": [
        "madapter.FixedMassMatrix"
      ]
    },
    {
      "page": "madapter.FullCov",
      "title": "Use the full covariance matrix as inverse mass matrix",
      "topics": [
        "madapter.FullCov"
      ]
    },
    {
      "page": "madapter.RMSProp",
      "title": "Use RMSProp to adapt the inverse mass matrix.",
      "topics": [
        "madapter.RMSProp"
      ]
    },
    {
      "page": "mcmc",
      "title": "Sample from a BNN using MCMC",
      "topics": [
        "mcmc"
      ]
    },
    {
      "page": "Normal",
      "title": "Create a Normal Prior",
      "topics": [
        "Normal"
      ]
    },
    {
      "page": "opt.ADAM",
      "title": "ADAM optimiser",
      "topics": [
        "opt.ADAM"
      ]
    },
    {
      "page": "opt.Descent",
      "title": "Standard gradient descent",
      "topics": [
        "opt.Descent"
      ]
    },
    {
      "page": "opt.RMSProp",
      "title": "RMSProp optimiser",
      "topics": [
        "opt.RMSProp"
      ]
    },
    {
      "page": "posterior_predictive",
      "title": "Draw from the posterior predictive distribution",
      "topics": [
        "posterior_predictive"
      ]
    },
    {
      "page": "prior_predictive",
      "title": "Sample from the prior predictive of a Bayesian Neural Network",
      "topics": [
        "prior_predictive"
      ]
    },
    {
      "page": "prior.gaussian",
      "title": "Use an isotropic Gaussian prior",
      "topics": [
        "prior.gaussian"
      ]
    },
    {
      "page": "prior.mixturescale",
      "title": "Scale Mixture of Gaussian Prior",
      "topics": [
        "prior.mixturescale"
      ]
    },
    {
      "page": "RNN",
      "title": "Create a RNN layer with `in_size` input, `out_size` hidden state and `act` activation function",
      "topics": [
        "RNN"
      ]
    },
    {
      "page": "sadapter.Const",
      "title": "Use a constant stepsize in mcmc",
      "topics": [
        "sadapter.Const"
      ]
    },
    {
      "page": "sadapter.DualAverage",
      "title": "Use Dual Averaging like in STAN to tune stepsize",
      "topics": [
        "sadapter.DualAverage"
      ]
    },
    {
      "page": "sampler.AdaptiveMH",
      "title": "Adaptive Metropolis Hastings as introduced in",
      "topics": [
        "sampler.AdaptiveMH"
      ]
    },
    {
      "page": "sampler.GGMC",
      "title": "Gradient Guided Monte Carlo",
      "topics": [
        "sampler.GGMC"
      ]
    },
    {
      "page": "sampler.HMC",
      "title": "Standard Hamiltonian Monte Carlo (Hybrid Monte Carlo).",
      "topics": [
        "sampler.HMC"
      ]
    },
    {
      "page": "sampler.SGLD",
      "title": "Stochastic Gradient Langevin Dynamics as proposed in Welling, M., & Teh, Y. W. (n.d.). Bayesian Learning via Stochastic Gradient Langevin Dynamics. 8.",
      "topics": [
        "sampler.SGLD"
      ]
    },
    {
      "page": "sampler.SGNHTS",
      "title": "Stochastic Gradient Nose-Hoover Thermostat as proposed in",
      "topics": [
        "sampler.SGNHTS"
      ]
    },
    {
      "page": "summary.BNN",
      "title": "Print a summary of a BNN",
      "topics": [
        "summary.BNN"
      ]
    },
    {
      "page": "tensor_embed_mat",
      "title": "Embed a matrix of timeseries into a tensor",
      "topics": [
        "tensor_embed_mat"
      ]
    },
    {
      "page": "to_bayesplot",
      "title": "Convert draws array to conform with `bayesplot`",
      "topics": [
        "to_bayesplot"
      ]
    },
    {
      "page": "Truncated",
      "title": "Truncates a Distribution",
      "topics": [
        "Truncated"
      ]
    },
    {
      "page": "vi.get_samples",
      "title": "Draw samples form a variational family.",
      "topics": [
        "vi.get_samples"
      ]
    }
  ],
  "_readme": "https://github.com/cran/BayesFluxR/raw/HEAD/README.md",
  "_rundeps": [
    "evaluate",
    "highr",
    "JuliaCall",
    "knitr",
    "Rcpp",
    "rjson",
    "xfun",
    "yaml"
  ],
  "_score": 1.6989700043360187,
  "_indexed": true,
  "_nocasepkg": "bayesfluxr",
  "_universes": [
    "cran"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.1.3",
      "date": "2026-06-01T09:50:13.000Z",
      "distro": "noble",
      "commit": "a0de5620ed1281167590875800d95fd16ce1440e",
      "fileid": "df3accb11bec1c9daee63e2708681f1c656859ba27bf46dccfd1b2b3be0fb391",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/26747409217"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "0.1.3",
      "date": "2026-06-01T09:50:04.000Z",
      "distro": "noble",
      "commit": "a0de5620ed1281167590875800d95fd16ce1440e",
      "fileid": "a69fbff581ef0e710050ebb18c823bf2bb965221bb45e469ce7187b9f63496eb",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/26747409217"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "0.1.3",
      "date": "2026-06-01T09:50:20.000Z",
      "commit": "a0de5620ed1281167590875800d95fd16ce1440e",
      "fileid": "6d2f5f0088d893533be8ef136741f7a3fe2be41446e55731de3736d9bf0e586e",
      "status": "success",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/26747409217"
    }
  ]
}