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.

Kalman Filter and Smoother for Exponential Family State Space
Models

package-ubuntu-20.04-r-4.0.5#cran#KFAS-1.4.5

## S3 method for class 'SSModel'
print(x, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p>SSModel object</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>Ignored.</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#KFAS-1.4.5#docs#print.SSModel

print.SSModel

Description

Usage

Arguments

KFAS

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`x`	SSModel object
`...`	Ignored.