seasonal is an easy-to-use and full-featured R interface to X-13ARIMA-SEATS, the seasonal adjustment software developed by the United States Census Bureau. The latest CRAN version of seasonal fixes several bugs and makes it easier to access output from multiple objects. See here for a complete list of changes.

The tsbox package provides a set of tools that are agnostic towards existing time series classes. The tools also allow you to handle time series as plain data frames, thus making it easy to deal with time series in a dplyr or data.table workflow.

seasonal is an easy-to-use and full-featured R-interface to X-13ARIMA-SEATS, the seasonal adjustment software developed by the United States Census Bureau. The latest CRAN version of seasonal makes it much easier to adjust multiple time series.

seasonal is an easy-to-use and full-featured R-interface to X-13ARIMA-SEATS, the seasonal adjustment software developed by the United States Census Bureau. The latest CRAN version of seasonal makes it much easier to adjust multiple time series.

Not having a time series at the desired frequency is a common problem for researchers and analysts. For example, instead of quarterly sales, they only have annual sales. Instead of a daily stock market index, they only have a weekly index. While there is no way to fully make up ...

Not having a time series at the desired frequency is a common problem for researchers and analysts. For example, instead of quarterly sales, they only have annual sales. Instead of a daily stock market index, they only have a weekly index. While there is no way to fully make up ...

The tsbox package makes life with time series in R easier. It is built around a set of functions that convert time series of different classes to each other. They are frequency-agnostic, and allow the user to combine time series of multiple non-standard and irregular frequencies. A detailed overview of ...

The tsbox package makes life with time series in R easier. It is built around a set of functions that convert time series of different classes to each other. They are frequency-agnostic, and allow the user to combine time series of multiple non-standard and irregular frequencies. A detailed overview of ...