mixediffusion - Mixed-Effects Diffusion Models with General Drift
Provides tools for likelihood-based inference in
one-dimensional stochastic differential equations with mixed
effects using expectation–maximization (EM) algorithms. The
package supports Wiener and Ornstein–Uhlenbeck diffusion
processes with user-specified drift functions, allowing
flexible parametric forms including polynomial, exponential,
and trigonometric structures. Estimation is performed via
Markov chain Monte Carlo EM.