Package: mssm
Type: Package
Title: Multivariate State Space Models
Version: 0.1.3
Authors@R: c(
  person("Benjamin", "Christoffersen", 
         email = "boennecd@gmail.com", 
         role = c("cre", "aut")), 
  person("Anthony", "Williams", 
         role = c("cph")))
Description: 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>.
License: GPL-2
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0), stats, graphics
LinkingTo: Rcpp, RcppArmadillo, testthat, nloptr (>= 1.2.0)
Imports: Rcpp, nloptr (>= 1.2.0)
RoxygenNote: 6.1.1
SystemRequirements: C++11
Suggests: testthat, microbenchmark, Ecdat
NeedsCompilation: yes
Packaged: 2019-11-06 13:08:03 UTC; boennecd
Author: Benjamin Christoffersen [cre, aut],
  Anthony Williams [cph]
Maintainer: Benjamin Christoffersen <boennecd@gmail.com>
Repository: CRAN
Date/Publication: 2019-11-07 00:20:02 UTC
Built: R 3.5.3; x86_64-w64-mingw32; 2020-04-22 07:53:43 UTC; windows
Archs: i386, x64
