Package: walker
Type: Package
Title: Bayesian Regression with Time-Varying Coefficients
Version: 0.2.1
Date: 2018-01-09
Author: Jouni Helske
Maintainer: Jouni Helske <jouni.helske@iki.fi>
Description: Bayesian dynamic regression models where the regression 
    coefficients can vary over time as random walks. 
    Gaussian, Poisson, and binomial observations are supported. 
    The Markov chain Monte Carlo computations are done using 
    Hamiltonian Monte Carlo provided by Stan, using a state space representation 
    of the model in order to marginalise over the coefficients for efficient sampling. 
    For non-Gaussian models, walker uses the importance sampling type estimators based on 
    approximate marginal MCMC as in Vihola, Helske, Franks (2017, <arXiv:1609.02541>).
License: GPL (>= 2)
Suggests: diagis, gridExtra, knitr (>= 1.11), rmarkdown (>= 0.8.1),
        testthat
Depends: R (>= 3.0.2), Rcpp (>= 0.12.9), bayesplot, rstan (>= 2.16.2)
Imports: dplyr, ggplot2, KFAS, methods
LinkingTo: StanHeaders (>= 2.16.0), rstan (>= 2.16.2), BH (>=
        1.62.0.1), Rcpp (>= 0.12.9), RcppArmadillo, RcppEigen (>=
        0.3.3.0)
SystemRequirements: C++11
VignetteBuilder: knitr
RoxygenNote: 6.0.1
ByteCompile: true
NeedsCompilation: yes
Packaged: 2018-01-09 14:09:30 UTC; jouhe21
Repository: CRAN
Date/Publication: 2018-01-09 17:27:02 UTC
Built: R 3.2.5; x86_64-apple-darwin13.4.0; 2018-01-10 15:29:13 UTC; unix
Archs: walker.so.dSYM
