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DESCRIPTION
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DESCRIPTION
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Package: bssm
Type: Package
Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space
Models
Version: 1.1.4
Date: 2021-04-13
Authors@R:
c(person(given = "Jouni",
family = "Helske",
role = c("aut", "cre"),
email = "jouni.helske@iki.fi",
comment = c(ORCID = "0000-0001-7130-793X")),
person(given = "Matti",
family = "Vihola",
role = "aut",
comment = c(ORCID = "0000-0002-8041-7222")))
Description: Efficient methods for Bayesian inference of state space models
via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel
importance sampling type weighted estimators
(Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>).
Gaussian, Poisson, binomial, negative binomial, and Gamma
observation densities and basic stochastic volatility models
with linear-Gaussian state dynamics,
as well as general non-linear Gaussian models and discretised
diffusion models are supported.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
Suggests: dplyr, ggplot2 (>= 2.0.0), Hmisc, KFAS (>= 1.2.1), knitr (>=
1.11), MASS, ramcmc, rmarkdown (>= 0.8.1), sde, sitmo, testthat
Imports: coda (>= 0.18-1), diagis, Rcpp (>= 0.12.3)
LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo
SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes)
RoxygenNote: 7.1.1
VignetteBuilder: knitr
BugReports: https://github.com/helske/bssm/issues
ByteCompile: true
Encoding: UTF-8
NeedsCompilation: yes