Contains data sets and scripts for analyzing time series in both the frequency and time domains including state space modeling as well as supporting the texts Time Series Analysis and Its Applications: With R Examples (5th ed, 2025) and Time Series: A Data Analysis Approach Using R, (2nd ed, 2026). Most scripts are designed to need minimal input to produce aesthetically pleasing output for ease of use in live demonstrations and in course work.
David Stoffer <stoffer@pitt.edu>
If loaded, the dplyr package corrupts the base scripts filter and lag among other things. In this case, whenever you analyze time series data, we suggest you either:
(1) Detach it if it's loaded but not being used:
detach(package:dplyr)
(2) If you want to use it, fix it:
library(dplyr, exclude = c("filter", "lag")) # load it without the culprits
dlag = dplyr::lag # and fix ...
dfilter = dplyr::filter # ... the blunders
then use dlag and dfilter for dplyr scripts
and lag and filter can be use as originally intended.
(3) Or just take back the commands:
filter = stats::filter
lag = stats::lag
In this case you can still use
dlag = dplyr::lag
dfilter = dplyr::filter
for dpylr.
| Package: | astsa |
| Type: | Package |
| Version: | 2.3 |
| Date: | 2026-01-01 |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
| LazyData: | yes |
You can find demonstrations of astsa capabilities at FUN WITH ASTSA.
The most recent version of the package can be found at https://github.com/nickpoison/astsa/.
In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.
The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.