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: | 0.1.3 |
| Depends: | R (≥ 3.5.0), stats, graphics |
| Imports: | Rcpp, nloptr (≥ 1.2.0) |
| LinkingTo: | Rcpp, RcppArmadillo, testthat, nloptr (≥ 1.2.0) |
| Suggests: | testthat, microbenchmark, Ecdat |
| Published: | 2019-11-07 |
| Author: | Benjamin Christoffersen [cre, aut], Anthony Williams [cph] |
| Maintainer: | Benjamin Christoffersen <boennecd at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Materials: | README NEWS |
| CRAN checks: | mssm results |
| Reference manual: | mssm.pdf |
| Package source: | mssm_0.1.3.tar.gz |
| Windows binaries: | r-devel: mssm_0.1.3.zip, r-release: mssm_0.1.3.zip, r-oldrel: mssm_0.1.3.zip |
| macOS binaries: | r-release: mssm_0.1.3.tgz, r-oldrel: mssm_0.1.3.tgz |
| Old sources: | mssm archive |
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