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KFAS: Kalman Filter and Smoother for Exponential Family State Space Models
State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient 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. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Getting started
Browse package contents
Package details |
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| Author | Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>) |
| Maintainer | Jouni Helske <jouni.helske@iki.fi> |
| License | GPL (>= 2) |
| Version | 1.5.1 |
| URL | https://github.com/helske/KFAS |
| Package repository | View on CRAN |
| Installation |
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