mssm: Multivariate State Space Models

Provides methods to perform parameter estimation and make analysis of multivariate observed outcomes through time which depends on a latent state variable. All methods scale well in the dimension of the observed outcomes at each time point. The package contains an implementation of a Laplace approximation, particle filters like suggested by Lin, Zhang, Cheng, & Chen (2005) <doi:10.1198/016214505000000349>, and the gradient and observed information matrix approximation suggested by Poyiadjis, Doucet, & Singh (2011) <doi:10.1093/biomet/asq062>.

Version: 0.1.6
Depends: R (≥ 3.5.0), stats, graphics
Imports: Rcpp, nloptr (≥ 1.2.0)
LinkingTo: Rcpp, RcppArmadillo, testthat, nloptr (≥ 1.2.0)
Suggests: testthat, Ecdat
Published: 2022-01-31
Author: Benjamin Christoffersen ORCID iD [cre, aut], Anthony Williams [cph]
Maintainer: Benjamin Christoffersen <boennecd at>
License: GPL-2
NeedsCompilation: yes
SystemRequirements: C++11
Materials: README NEWS
In views: TimeSeries
CRAN checks: mssm results


Reference manual: mssm.pdf


Package source: mssm_0.1.6.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mssm_0.1.6.tgz, r-oldrel (arm64): mssm_0.1.6.tgz, r-release (x86_64): mssm_0.1.6.tgz, r-oldrel (x86_64): mssm_0.1.6.tgz
Old sources: mssm archive


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