Learn R Programming

⚠️There's a newer version (1.5.1) of this package.Take me there.

KFAS (version 0.9.9)

Kalman Filter and Smoother for Exponential Family State Space Models.

Description

Package KFAS provides functions for Kalman filtering, smoothing, forecasting and simulation of Gaussian, Poisson and Binomial state space models with exact diffuse initialization when distributions of some or all elements of initial state vector are unknown.

Copy Link

Version

Install

install.packages('KFAS')

Monthly Downloads

21,166

Version

0.9.9

License

GPL (>= 2)

Maintainer

Jouni Helske

Last Published

July 6th, 2012

Functions in KFAS (0.9.9)

KFAS: Functions for Gaussian and Non-Gaussian State Space Models

Print Ouput of Kalman Filter and Smoother

Plot Approximating Gaussian State Space Model

Transform the SSModel object with multivariate observations

Linear Gaussian Approximation for Non-Gaussian State Space Model

Create a State Space Representation of ARIMA Model

Maximum Likelihood Estimation of a State Space Model

Kalman Filter and Smoother with Exact Diffuse Initialization for Exponential Family State Space Models

Simulation of a Gaussian State Space Model

Create a State Space Model Representation of Linear Regression Model

Oxford-Cambridge boat race results 1829-2000

Log-likelihood of the State Space Model.

Plot Ouput of Kalman Filter and Smoother

Extract Standardized Residuals of Kalman Filter and Smoother output

Combine State Space Model Objects of class SSModel

LDL Decomposition of a Matrix

Estimate the Missing Observations of a State Space Model

Print SSModel Object

Importance Sampling of Non-Gaussian State Space Model

Create a State Space Model Object of class SSModel

Create a Structural Time Series State Space Model

Two series of average global temperature deviations for years 1880-1987

Extract the filtered or smoothed signal of a State Space Model