EMLI: Computationally Efficient Maximum Likelihood Identification of Linear Dynamical Systems

Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models that represent linear dynamical systems. Currently, one such algorithm is implemented for the one-dimensional cumulative structural equation model with shock-error output measurement equation and assumptions of normality and independence. The corresponding scientific paper is yet to be published, therefore the relevant reference will be provided later.

Version: 0.2.0
Imports: stats
Published: 2022-11-20
Author: Vytautas Dulskis [cre, aut], Leonidas Sakalauskas [aut]
Maintainer: Vytautas Dulskis <vytautas.dulskis at gmail.com>
License: GPL-2
Copyright: Vilnius University Institute of Data Science and Digital Technologies
NeedsCompilation: no
Materials: NEWS
CRAN checks: EMLI results

Documentation:

Reference manual: EMLI.pdf

Downloads:

Package source: EMLI_0.2.0.tar.gz
Windows binaries: r-devel: EMLI_0.2.0.zip, r-release: EMLI_0.1.0.zip, r-oldrel: EMLI_0.2.0.zip
macOS binaries: r-release (arm64): EMLI_0.2.0.tgz, r-oldrel (arm64): EMLI_0.2.0.tgz, r-release (x86_64): EMLI_0.2.0.tgz, r-oldrel (x86_64): EMLI_0.2.0.tgz
Old sources: EMLI archive

Linking:

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