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  "Package": "uqsa",
  "Type": "Package",
  "Title": "Uncertainty Quantification and Global Sensitivity Analysis",
  "Version": "0.8.3",
  "Authors@R": "c(\nperson(\"Alexandra\",\"Jauhiainen\",email=\"Alexandra.Jauhiainen@astrazeneca.com\",role=c(\"aut\")),\nperson(\"Olivia\",\"Eriksson\",email=\"olivia@kth.se\",role=c(\"aut\",\"ctb\",\"cph\")),\nperson(\"Federica\",\"Milinanni\",email=\"fedmil@kth.se\",role=\"aut\"),\nperson(\"Andrei\",\"Kramer\",email=\"andreikr@kth.se\",role=c(\"cre\")))",
  "Description": "In the field of systems biology, chemical reaction\nnetworks are modeled in various ways, two of those are: (i)\nstochastic simulations (e.g. Gillespie algorithm) and (ii)\nordinary differential equations. In this package we use a\nsimple tabular model description of reaction systems and\nautomatically generate C code for either solver type. We use\nthe ordinary differential equation solvers from the GNU\nScientific Library and provide an interface that deals with\nlists of simulation experiments. Each simulation experiment\ncontains both the data, and instructions for the model to\nreplicate the data. We use approximate Bayesian computation\nmethods (combined with Markov chain Monte Carlo and sequential\nMonte Carlo, particle filters) as well as classic methods such\nas Random Walk Metropolis (Gaussian transition kernel) and\nSimplified Manifold Metropolis adjusted Langevin algorithm for\na Bayesian investigation of the model´s parameter space.\nExperiments can be evaluated in a sequence; intermediate\nprobability densities are modeled using the 'VineCopula'\npackage. The package is also intended to be useful in an HPC\nenvironment, with some functions that use 'pbdMPI'\ncapabilities.",
  "Language": "en-US",
  "License": "GPL (>=3)",
  "Encoding": "UTF-8",
  "SystemRequirements": "GSL (>= 2.7)",
  "Roxygen": "list(markdown = TRUE)",
  "Config/testthat/edition": "3",
  "VignetteBuilder": "knitr",
  "URL": "https://icpm-kth.github.io/uqsa/",
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  "Repository": "https://icpm-kth.r-universe.dev",
  "Date/Publication": "2026-10-20 13:00:29 UTC",
  "RemoteUrl": "https://github.com/icpm-kth/uqsa",
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  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-10-20 15:28:00 UTC",
    "User": "root"
  },
  "Author": "Alexandra Jauhiainen [aut],\nOlivia Eriksson [aut, ctb, cph],\nFederica Milinanni [aut],\nAndrei Kramer [cre]",
  "Maintainer": "Andrei Kramer <andreikr@kth.se>",
  "_user": "icpm-kth",
  "_type": "src",
  "_file": "uqsa_0.8.3.tar.gz",
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  "_sha256": "13cc210a00d0720dcdf451a000f96d7206ad0ba7d22427e8e9bda489faf6e826",
  "_expires": "2027-01-28T15:39:14.000Z",
  "_created": "2026-10-20T15:28:00.000Z",
  "_published": "2026-10-20T15:39:20.002Z",
  "_upstream": "https://github.com/icpm-kth/uqsa",
  "_status": "success",
  "_maintainer": {
    "name": "Andrei Kramer",
    "email": "andreikr@kth.se"
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    "author": "Andrei Kramer <andreikr@kth.se>",
    "committer": "Andrei Kramer <andreikr@kth.se>",
    "message": "fixed a bug related to the parsing of reaction rates like '2*A' where there is a coefficient\n",
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  "_exports": [
    "%@%",
    "%as%",
    "%has%",
    "%otherwise%",
    "abc_mcmc",
    "ABCSMC",
    "as_cme",
    "as_ode",
    "base<-",
    "c_path",
    "c_path<-",
    "clear_yacas_environment",
    "column",
    "conservation_law_analysis",
    "CRNN",
    "dCopulaPrior",
    "defaultDistance",
    "determinePrefix",
    "dNormalPrior",
    "dUniformPrior",
    "exact_normal_kld",
    "experiments",
    "fi",
    "fitCopula",
    "formulae",
    "gatherReplicas",
    "gatherSample",
    "generate_code",
    "gllf",
    "gNormalPrior",
    "gsa_binning",
    "gsa_saltelli",
    "gsl_odeiv2_CRNN",
    "gsl_odeiv2_fi",
    "high_level_metropolis",
    "high_level_smmala",
    "highCor",
    "kinetic_law_matrix",
    "KLD",
    "linear_scale",
    "ll",
    "loadSample_mpi",
    "log10ParMap",
    "log10ParMapJac",
    "log2ParMap",
    "log2ParMapJac",
    "logLikelihoodFunc",
    "logParMap",
    "logParMapJac",
    "makeObjective",
    "mcmc",
    "mcmc_init",
    "mcmc_mpi",
    "method",
    "metropolis_update",
    "model_from_tsv",
    "modify<-",
    "name_method",
    "onlyCoefficients",
    "onlyNames",
    "parse_concise",
    "pc_dist",
    "rCopulaPrior",
    "replace_powers",
    "rNormalPrior",
    "rUniformPrior",
    "saltelli_prior",
    "scrnn",
    "sensitivity.graph",
    "shlib",
    "showPosterior",
    "simfi",
    "simstoch",
    "simulator.c",
    "small",
    "smmala_update",
    "so_path",
    "so_path<-",
    "standard_error_matrix",
    "stoichiometric_matrix",
    "stoichiometry",
    "tune_step_size",
    "uncertainty",
    "unit_as_character",
    "unit.from.string",
    "unit.id",
    "units_from_table",
    "uqsa_example",
    "values",
    "write_and_compile",
    "write_c_code",
    "yJacobian"
  ],
  "_help": [
    {
      "page": "abc_mcmc",
      "title": "Performs Approximate Bayesian Computation Sampling of Model Parameters via Markov Chain Monte Carlo",
      "topics": [
        "abc_mcmc"
      ]
    },
    {
      "page": "ABCSMC",
      "title": "Performs Approximate Bayesian Computation as a Particle Filter",
      "topics": [
        "ABCSMC"
      ]
    },
    {
      "page": "as_cme",
      "title": "Interprets the provided biological reaction model as stochastic",
      "topics": [
        "as_cme"
      ]
    },
    {
      "page": "as_ode",
      "title": "Interprets a model as an ODE",
      "topics": [
        "as_ode"
      ]
    },
    {
      "page": "c_path",
      "title": "Retrieves information about the model's C code",
      "topics": [
        "c_path"
      ]
    },
    {
      "page": "c_path-set",
      "title": "Adds information about the model's C code",
      "topics": [
        "c_path<-"
      ]
    },
    {
      "page": "column",
      "title": "Retrieves j-th column with names",
      "topics": [
        "column"
      ]
    },
    {
      "page": "CRNN",
      "title": "Creates C code for a chemical reaction neural network",
      "topics": [
        "CRNN"
      ]
    },
    {
      "page": "dCopulaPrior",
      "title": "Creates a prior probability density function",
      "topics": [
        "dCopulaPrior"
      ]
    },
    {
      "page": "dNormalPrior",
      "title": "Creates the density function of a multivariate normal distribution with independent components",
      "topics": [
        "dNormalPrior"
      ]
    },
    {
      "page": "dUniformPrior",
      "title": "Creates a uniform density function",
      "topics": [
        "dUniformPrior"
      ]
    },
    {
      "page": "experiments",
      "title": "Extracts measured data and simulation experiment instructions",
      "topics": [
        "experiments"
      ]
    },
    {
      "page": "fitCopula",
      "title": "Makes a Probability Density Estimator from a Sample",
      "topics": [
        "fitCopula"
      ]
    },
    {
      "page": "formulae",
      "title": "Finds a column that contains some kind of mathematic expression in a data.frame",
      "topics": [
        "formulae"
      ]
    },
    {
      "page": "generate_code",
      "title": "Constructs Code",
      "topics": [
        "generate_code"
      ]
    },
    {
      "page": "gNormalPrior",
      "title": "Creates the gradient function of a multivariate normal distribution with independent components, in log-space",
      "topics": [
        "gNormalPrior"
      ]
    },
    {
      "page": "gsa_binning",
      "title": "Performs global sensitivity analysis using the binning method",
      "topics": [
        "gsa_binning"
      ]
    },
    {
      "page": "gsa_saltelli",
      "title": "Outputs the global sensitivity scores SI and SIT, calculated by the Sobol-Homma-Saltelli method",
      "topics": [
        "gsa_saltelli"
      ]
    },
    {
      "page": "gsl_odeiv2_CRNN",
      "title": "Simulates a CRNN ode model",
      "topics": [
        "gsl_odeiv2_CRNN"
      ]
    },
    {
      "page": "gsl_odeiv2_fi",
      "title": "Simulates an ode model",
      "topics": [
        "gsl_odeiv2_fi"
      ]
    },
    {
      "page": "high_level_metropolis",
      "title": "Determines a likely Metropolis setup based on the information in the model",
      "topics": [
        "high_level_metropolis"
      ]
    },
    {
      "page": "high_level_smmala",
      "title": "Determines a likely SMMALA setup based on the information in the model",
      "topics": [
        "high_level_smmala"
      ]
    },
    {
      "page": "kinetic_law_matrix",
      "title": "Represents the kinetic laws of the model as a two column matrix",
      "topics": [
        "kinetic_law_matrix"
      ]
    },
    {
      "page": "linear_scale",
      "title": "Interprets a character vector as names of logarithms",
      "topics": [
        "linear_scale"
      ]
    },
    {
      "page": "logLikelihoodFunc",
      "title": "Constructs a custom log-likelihood function",
      "topics": [
        "logLikelihoodFunc"
      ]
    },
    {
      "page": "makeObjective",
      "title": "Creates objective functions from ingredients",
      "topics": [
        "makeObjective"
      ]
    },
    {
      "page": "mcmc",
      "title": "Creates a Markov Chain Monte Carlo sampler",
      "topics": [
        "mcmc"
      ]
    },
    {
      "page": "mcmc_init",
      "title": "Initializes the Markov chain",
      "topics": [
        "mcmc_init"
      ]
    },
    {
      "page": "mcmc_mpi",
      "title": "Creates the MPI version of an MCMC sampler",
      "topics": [
        "mcmc_mpi"
      ]
    },
    {
      "page": "method",
      "title": "Finds the method id given a named method",
      "topics": [
        "method"
      ]
    },
    {
      "page": "metropolis_update",
      "title": "Performs an MCMC update using the Metropolis rule",
      "topics": [
        "metropolis_update"
      ]
    },
    {
      "page": "model_from_tsv",
      "title": "Loads the content from a series of TSV files",
      "topics": [
        "model_from_tsv"
      ]
    },
    {
      "page": "name_method",
      "title": "Reverse look-up of method name from key",
      "topics": [
        "name_method"
      ]
    },
    {
      "page": "parse_concise",
      "title": "Reads Concise Error Notation",
      "topics": [
        "parse_concise"
      ]
    },
    {
      "page": "pc_dist",
      "title": "Plots a sample in parallel coordinates",
      "topics": [
        "pc_dist"
      ]
    },
    {
      "page": "rCopulaPrior",
      "title": "Returns a function that generates random values from the copula model",
      "topics": [
        "rCopulaPrior"
      ]
    },
    {
      "page": "replace_powers",
      "title": "Interprets caret notation of arithmetic powers for C",
      "topics": [
        "replace_powers"
      ]
    },
    {
      "page": "rNormalPrior",
      "title": "Returns a random vector generator",
      "topics": [
        "rNormalPrior"
      ]
    },
    {
      "page": "rUniformPrior",
      "title": "Returns a random vector generator",
      "topics": [
        "rUniformPrior"
      ]
    },
    {
      "page": "saltelli_prior",
      "title": "Creates prior samples for the Sobol-Homma-Saltelli global sensitivity analysis",
      "topics": [
        "saltelli_prior"
      ]
    },
    {
      "page": "scrnn",
      "title": "Returns a closure around gsl_odeiv2_CRNN()",
      "topics": [
        "scrnn"
      ]
    },
    {
      "page": "sensitivity.graph",
      "title": "Plot the result of global sensitivity analysis",
      "topics": [
        "sensitivity.graph"
      ]
    },
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