README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/"
)
```

# stemr <img src="man/figures/logo.png" align="right" alt="" width="120" />

## Baysian Inference for Stochastic Epidemic Models via the Linear Noise Approximation

The __stemr__ R package implements a Bayesian data augmentation algorithm for 
fitting stochastic epidemic models (SEMs) with arbitrary dynamics to partially 
observed epidemic count data via the linear noise approximation (LNA). This 
repository and package exist to expose and archive the code used in the paper, 
"A Linear Noise Approximation for Stochastic Epidemic Models Fit to Partially 
Observed Incidence Counts" by J. Fintzi, J. Wakefield, and V.N. Minin. The 
package provides facilities for exact simulation of SEMs via Gillespie's direct 
algorithm, and for simulation of approximate epidemic paths via the LNA. 
Inference is carried out using a Bayesian data augmentation algorithm. LNA paths 
are sampled via the elliptical slice sampling algorithm of Murray et al. (2010), 
and the package  offers several choices for adaptive and fixed kernel MCMC 
algorithms for parameter updates. The package also allows for time varying 
covariates, time varying parameters, and deterministic forcings. These 
functionalities are not yet fully documented, but sample code is available in 
the '/.development_files' directory on the GitHub repository.

## Package installation
To install the `stemr` package, clone this repository and build the package from sources. You should be able to rebuild in the usual way once you clone the package repo and install the other dependencies (`odeintr`,  `extraDistr`, `Rcpp`, `RcppArmadillo`, and `BH`). Computationally intensive components in `stemr` package are implemented in C++. Hence, it is important to also make sure that your C++ toolchain is set up properly, e.g., by following instructions given in the [Stan](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started) documentation, and that Rtools has been added to your system path. If you are working on a Windows machine, you may need to take additional steps to ensure your toolset is in order. 

## Vignettes and Simulation Code
The code for reproducing the simulations in Fintzi, et. al (2020) can be found in the 
['/.development_files/incidence_lna_simulations'](https://github.com/fintzij/stemr/tree/master/.development_files/incidence_lna_simulations) folder.
There are two vignettes included in this package to help familiarize users
with its basic functionality and that reproduce models the SEMs fit via the LNA
in Fintzi, et al. (2020). The [stemr](https://github.com/fintzij/stemr/blob/master/vignettes/stemr.Rmd) vignette provides an introduction to
the `stemr` package, and demonstrates how to simulate from and fit an SIR model
via the LNA and ODE. The [ebola_westafrica](https://github.com/fintzij/stemr/blob/master/vignettes/ebola_westafrica.Rmd) vignette demonstrates how to simulate
from and fit a multi-country model for Ebola transmission, and then provides code
to fit the model to data from the 2014-2015 outbreak in West Africa.