  This paper presents the R package GAS for the analysis of time series under
the Generalized Autoregressive Score (GAS) framework of Creal et al. (2013) and
Harvey (2013). The distinctive feature of the GAS approach is the use of the
score function as the driver of time-variation in the parameters of nonlinear
models. The GAS package provides functions to simulate univariate and
multivariate GAS processes, estimate the GAS parameters and to make time series
forecasts. We illustrate the use of the GAS package with a detailed case study
on estimating the time-varying conditional densities of a set of financial
assets.
