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) , and the gradient and observed information matrix approximation suggested by Poyiadjis, Doucet, & Singh (2011) .


Reference manual

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0.1.5 by Benjamin Christoffersen, 22 days ago

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Authors: Benjamin Christoffersen [cre, aut] , Anthony Williams [cph]

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL-2 license

Imports Rcpp, nloptr

Depends on stats, graphics

Suggests testthat, Ecdat

Linking to Rcpp, RcppArmadillo, testthat, nloptr

System requirements: C++11

See at CRAN