This repository houses the
spmrf, which is used for fitting Bayesian nonparametric adaptive smoothing models as described in Faulkner and Minin (2018). The
spmrf package interfaces with Stan, which is a C++ package for performing Bayesian inference using Hamiltonian Monte Carlo (see http://mc-stan.org/). Stan can be interfaced with the R package
rstan, and thus the
spmrf package depends on the
rstan package to fit models.
rstanand install package
install.packagesfunction. Note that if you do not already have
rstaninstalled, you may need to install additional packages such as
Rtoolsif using a Windows platform, or
Xcodeif you are using a Mac. See the
rstanprerequisites for more information. If you want the vignettes, you may also need to install the
spmrffrom GitHub using either 1)
install_github("jrfaulkner/spmrf", build_vignettes=TRUE)if you want the vignette documentation which provides examples of using
spmrf. Note that building vignettes will make the load take a little longer.
The following vignettes provide some examples using the
spmrf package with step-by-step instructions and R code.
Faulkner, J. R., and V. N. Minin. 2018. Locally adaptive smoothing with Markov random fields and shrinkage priors. Bayesian Analysis 13(1):225-252.
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