{ "_id": "6ad620f65a02066177b9ae35", "Package": "uqsa", "Type": "Package", "Title": "Uncertainty Quantification and Global Sensitivity Analysis", "Version": "0.8.1", "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. 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