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We present an R-package developed for modeling of biochemical reactionnetworks, uncertainty quantification (UQ) and sensitivity analysis (SA).Estimating parameters and quantifying their uncertainty (and resultingprediction uncertainty), is required for data-driven systems biology modeling.Sampling methods need to be efficient when confronted with high-dimensional,correlated parameter distributions. We have developed the UQSA package to befast for this problem class and work well with other tools for modelling. Weaim for simplicity, and part of that is our use of the SBtab format for theunified storage of model and data. Our tool-set is modular enough, that partscan be replaced. We use intermediate formats that are not hidden from the userto make this feasible. UQ is performed through Markov chain Monte Carlo (MCMC)sampling in an Approximate Bayesian Computation (ABC) setting. This can befollowed by a variance-decomposition based global sensitivity analysis. Ifneeded, complex parameter distributions can be described, evaluated, andsampled from, with the help of Vine-copulas that are available in R. Thisapproach is especially useful when new experimental data become available, anda previously calibrated model needs to be updated.  Implementation: R is a high-level language and allows the use ofsophisticated statistical methods. The ode solver we used is written in C(gsl_odeiv2, interface to R is ours). We use the SBtab tabular format for themodel description, as well as the data and an event system to be able to modelinputs frequently encountered in systems biology and neuroscience. The code hasbeen tested on one node with 256 cores of a computing cluster, but smallerexamples are included in the repository that can be run on a laptop.  Source code: https://github.com/icpm-kth/uqsa

UQSA -- An R-Package for Uncertainty Quantification and Sensitivity Analysis for Biochemical Reaction Network Models