This is a Stan implementation of Drew Linzer's dynamic Bayesian election forecasting model, with some tweaks to incorporate national poll data, pollster house effects, correlated priors on state-by-state election results and comovement of public opinion across states.

The model is presented briefly at the end of report.html.

runmodel.R downloads poll data from the HuffPost Pollster API, processes the data, and runs the Stan model in state and national polls.stan.

report.Rmd is a Rmarkdown document used to automatically generate the graphs/tables/maps in report.html and relies on graphs.R.

Reproducing the analysis

1. Clone this repository.
2. Install the required R packages (listed below)
3. Remove or comment out the lines in graphs.R, runmodel.R and report.Rmd that read setwd("~/GitHub/polls").
4. Optionally, modify line 2 of runmodel.R to use a number of cores that is less than all available (e.g., `options(mc.cores = parallel::detectCores()

• 1)` to leave one core free for multitasking

5. In an R session, run source("runmodel.R")

Required R packages

curl
dplyr
DT
ggplot2
ggrepel
knitr
lubridate
mapproj
maps
mvtnorm
purrr
reshape2
rmarkdown
rstan
shinystan
stringr
tidyr