KFAS 1.3.7

Kalman Filter and Smoother for Exponential Family State Space Models

Released Jun 10, 2019 by Jouni Helske

This package can be loaded by Renjin but there was an error compiling C/FORTRAN sources and 21 out 24 tests failed. An older version of this package is more compatible with Renjin.

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) for details.



This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

    <name>bedatadriven public repo</name>

View build log

Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:


Test Results

This package was last tested against Renjin 0.9.2726 on Jul 13, 2019.