  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.
