  Time-varying parameter (TVP) models are widely used in time series analysis
to flexibly deal with processes which gradually change over time. However, the
risk of overfitting in TVP models is well known. This issue can be dealt with
using appropriate global-local shrinkage priors, which pull time-varying
parameters towards static ones. In this paper, we introduce the R package
shrinkTVP (Knaus, Bitto-Nemling, Cadonna, and Fr\"uhwirth-Schnatter 2019),
which provides a fully Bayesian implementation of shrinkage priors for TVP
models, taking advantage of recent developments in the literature, in
particular that of Bitto and Fr\"uhwirth-Schnatter (2019). The package
shrinkTVP allows for posterior simulation of the parameters through an
efficient Markov Chain Monte Carlo (MCMC) scheme. Moreover, summary and
visualization methods, as well as the possibility of assessing predictive
performance through log predictive density scores (LPDSs), are provided. The
computationally intensive tasks have been implemented in C++ and interfaced
with R. The paper includes a brief overview of the models and shrinkage priors
implemented in the package. Furthermore, core functionalities are illustrated,
both with simulated and real data.
