This repository houses the `R`

package `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.

- Install package dependency
`rstan`

and install package`devtools`

using`install.packages`

function. Note that if you do not already have`rstan`

installed, you may need to install additional packages such as`Rtools`

if using a Windows platform, or`Xcode`

if you are using a Mac. See the`rstan`

prerequisites for more information. If you want the vignettes, you may also need to install the`rmarkdown`

package - Load
`devtools`

using`library(devtools)`

. - Install
`spmrf`

from GitHub using either 1)`install_github("jrfaulkner/spmrf")`

or 2)`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.

- Introduction to spmrf
- Coal Mining Example
- Unequal Grid and Covariates Example
- Phylodynamics Examples

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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