Abstract
ctsmr is an R package providing a general framework for identifying and estimating partially observed continuous-discrete time gray-box models. The estimation is based on maximum likelihood principles and Kalman filtering efficiently implemented in Fortran. This paper briefly demonstrates how to construct a Continuous Time Stochastic Model using multivariate time series data, and how to estimate the embedded parameters. The setup provides a unique framework for statistical modeling of physical phenomena, and the approach is often called grey box modeling. Finally three examples are provided to demonstrate the capabilities of ctsmr.
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