Kalman Filter and Smoother for Exponential Family State Space
Models
Description
State space modelling is an efficient and flexible method for
statistical inference of a broad class of time series and other data. KFAS
includes fast functions for Kalman filtering, smoothing, forecasting, and
simulation of multivariate exponential family state space models, with
observations from Gaussian, Poisson, binomial, negative binomial, and gamma
distributions.