Kalman Filter and Smoother for Exponential Family State Space Models
Linear Gaussian Approximation for Exponential Family State Space Model
Mapping real valued parameters to stationary region
Test whether object is a valid SSModel object
Smoothed Estimates or One-step-ahead Predictions of States
Confidence Intervals of Smoothed States
Extract or Replace Parts of a State Space Model
Maximum Likelihood Estimation of a State Space Model
Smoothed Estimates or One-step-ahead Predictions of Fitted Values
Extract Hat Values from KFS Output
Importance Sampling of Exponential Family State Space Model
Defunct Functions of Package KFAS
KFAS: Functions for Exponential Family State Space Models
Kalman Filter and Smoother with Exact Diffuse Initialization for Expon...
LDL Decomposition of a Matrix
Log-likelihood of the State Space Model.
Multivariate Innovations
Diagnostic Plots of State Space Models
State Space Model Predictions
Print Ouput of Kalman Filter and Smoother
Print SSModel Object
Rename the States of SSModel Object
Extract Residuals of KFS output
Extract Standardized Residuals from KFS output
Extracting the Partial Signal Of a State Space Model
Simulation of a Gaussian State Space Model
Create a State Space Model Object of Class SSModel
Transform Multivariate State Space Model for Sequential Processing
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.