  Generalized autoregressive score (GAS) models are a class of
observation-driven time series models that employ the score to dynamically
update time-varying parameters of the underlying probability distribution. GAS
models have been extensively studied and numerous variants have been proposed
in the literature to accommodate diverse data types and probability
distributions. This paper introduces the gasmodel package, which has been
designed to facilitate the estimation, forecasting, and simulation of a wide
range of GAS models. The package provides a rich selection of distributions,
offers flexible options for specifying dynamics, and allows to incorporate
exogenous variables. Model estimation utilizes the maximum likelihood method.
