atsar

R-CMD-check

The atsar R package implements Bayesian time series models using Stan, primarily for illustrative purposes and teaching (University of Washington’s Fish 550, Winter quarters). The Stan webpage, and appropriate citation guidelines are here.

INSTALL

You can install the development version of the package with:

# install.packages("devtools")
devtools::install_github("atsa-es/atsar")

EXAMPLE

Simulate data:

library(rstan)
#> Loading required package: StanHeaders
#> Loading required package: ggplot2
#> rstan (Version 2.21.2, GitRev: 2e1f913d3ca3)
#> For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores()).
#> To avoid recompilation of unchanged Stan programs, we recommend calling
#> rstan_options(auto_write = TRUE)
library(atsar)
set.seed(123)
s = cumsum(rnorm(50))
plot(s)

Fit several models to this data:

# Regression, no slope
regression_model = fit_stan(y = s, x = model.matrix(lm(s~1)), model_name="regression")

# Regression, with slope
regression_model = fit_stan(y = s, x = model.matrix(lm(s~seq(1,length(s)))), model_name="regression")

# AR(1) time series model
ar1_model = fit_stan(y = s, est_drift=FALSE, P = 1, model_name = "ar")

# ARMA(1,1) time series model
arma1_model = fit_stan(y = s, model_name = "arma11")

# univariate ss model -- without drift but mean reversion estimated
ss_model = fit_stan(y = s, model_name = "ss_ar", est_drift=FALSE)

To see the Stan mode code behind each of these, look in the inst/stan folder on the GitHub repository. Note that fit_stan.R does some data preparation to deal with Stan not accepting NAs in the data.

DOCUMENTATION

CITATION

Ward, E.J., M.D. Scheuerell, and E.E. Holmes. 2018. ‘atsar’: Applied Time Series Analysis in R: an introduction to time series analysis for ecological and fisheries data with Stan. DOI

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