  Score-driven models, also known as generalized autoregressive score models,
represent a class of observation-driven time series models. They possess
powerful properties, such as the ability to model different conditional
distributions and to consider time-varying parameters within a flexible
framework. In this paper, we present ScoreDrivenModels.jl, an open-source Julia
package for modeling, forecasting, and simulating time series using the
framework of score-driven models. The package is flexible with respect to model
definition, allowing the user to specify the lag structure and which parameters
are time-varying or constant. It is also possible to consider several
distributions, including Beta, Exponential, Gamma, Lognormal, Normal, Poisson,
Student's t, and Weibull. The provided interface is flexible, allowing
interested users to implement any desired distribution and parametrization.
