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• Install
• Documentation
• Example
• Citation
• License
• GitHub

The atsar R package implements Bayesian time series models using Stan, primarily for illustrative purposes and teaching (University of Washington’s Fish 507, Winter quarters). The Stan webpage, and appropriate citation guidelines are here.

INSTALL

Install atsar from the atsa-es r-universe repository (binaries for Windows and Mac-Intel)

install.packages('atsar', repos = c('https://atsa-es.r-universe.dev', 'https://cloud.r-project.org'))

You can build the development version of the package from the source here. Note you need to use this if you have a M1/M2 Mac.

# install.packages("remotes")
remotes::install_github("nwfsc-timeseries/atsar")

EXAMPLE

Simulate data:

library(rstan)
#> Warning: package 'rstan' was built under R version 4.3.2
#> Loading required package: StanHeaders
#> Warning: package 'StanHeaders' was built under R version 4.3.2
#> 
#> rstan version 2.32.6 (Stan version 2.32.2)
#> 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)
#> For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
#> change `threads_per_chain` option:
#> rstan_options(threads_per_chain = 1)
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

• ATSA lab book - Many applications are covered in our Applied Time Series Analysis book developed from the labs in our course.
• ATSA course website - We have lectures and all material from our course on our course website.
• Additional information can be found on the NWFSC time series page which includes several additional books and packages, NWFSC time series page

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.

NOAA Disclaimer

This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project code is provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.

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