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arXiv · 2611.07096

dsp: An R Package for Bayesian Time Series Modeling with the Dynamic Shrinkage Process

Jason B. Cho·Daniel R. Kowal·Haoxuan Wu·David S. Matteson·Toryn L. J. Schafer
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Abstract

dsp provides a unified state-space framework for modeling nonstationary time series using the Dynamic Shrinkage Process (DSP), a class of global-local shrinkage priors. DSP adaptively regularizes time-varying parameters to shrink noise while preserving abrupt changes and other local structure. The package supports Gaussian and count-data smoothing, changepoint detection, time-varying parameter regression, and volatility estimation, with posterior predictive draws available for missing-value imputation and forecasting. Nonstationary time series often contain periods of relative stability punctuated by abrupt changes in mean or variance. dsp offers a common interface for model specification, posterior computation, summarization, and visualization across models that were previously developed separately in the literature. Core functionality is implemented in C++ via Rcpp for computational efficiency. We describe the underlying models, package design, and the main functions for fitting, summarizing, and plotting posterior results.

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Jason B. Cho, Daniel R. Kowal, Haoxuan Wu, David S. Matteson, Toryn L. J. Schafer. 2026-11-04. dsp: An R Package for Bayesian Time Series Modeling with the Dynamic Shrinkage Process. https://arxiv.org/abs/2611.07096

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