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, (1st ed, 2019).
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:
detach(package:dplyr)
(2) If you want to use dplyr, fix it:
library(dplyr, exclude = c("filter", "lag")) # load it but remove the culprits
Lag <- dplyr::lag # and do what the dplyr ...
Filter <- dplyr::filter # ... maintainer refuses to do
then use Lag and Filter 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
Lag <- dplyr::lag
and
Filter <- dplyr::filter
for dpylr.
| Package: | astsa |
| Type: | Package |
| Version: | 2.2 |
| Date: | 2025-01-15 |
| 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/.