The 'actfts' package provides tools for performing autocorrelation analysis of time series data. It includes functions to compute and visualize the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Additionally, it performs the Dickey-Fuller, KPSS, and Phillips-Perron unit root tests to assess the stationarity of time series. Theoretical foundations are based on Box and Cox (1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Box and Jenkins (1976) <isbn:978-0-8162-1234-2>, and Box and Pierce (1970) <doi:10.1080/01621459.1970.10481180>. Statistical methods are also drawn from Kolmogorov (1933) <doi:10.1007/BF00993594>, Kwiatkowski et al. (1992) <doi:10.1016/0304-4076(92)90104-Y>, and Ljung and Box (1978) <doi:10.1093/biomet/65.2.297>. The package integrates functions from 'forecast' (Hyndman & Khandakar, 2008) <https://CRAN.R-project.org/package=forecast>, 'tseries' (Trapletti & Hornik, 2020) <https://CRAN.R-project.org/package=tseries>, 'xts' (Ryan & Ulrich, 2020) <https://CRAN.R-project.org/package=xts>, and 'stats' (R Core Team, 2023) <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html>. Additionally, it provides visualization tools via 'plotly' (Sievert, 2020) <https://CRAN.R-project.org/package=plotly> and 'reactable' (Glaz, 2023) <https://CRAN.R-project.org/package=reactable>. The package also incorporates macroeconomic datasets from the U.S. Bureau of Economic Analysis: Disposable Personal Income (DPI) <https://fred.stlouisfed.org/series/DPI>, Gross Domestic Product (GDP) <https://fred.stlouisfed.org/series/GDP>, and Personal Consumption Expenditures (PCEC) <https://fred.stlouisfed.org/series/PCEC>.

A tool to obtain activity counts, originally a translation of the 
  'python' package 'agcounts' <https://github.com/actigraph/agcounts>. This tool
  allows the processing of data from any accelerometer brand, with a more flexible 
  approach to handle different sampling frequencies.

Provides sleep duration estimates using a Pruned Dynamic
  Programming (PDP) algorithm that efficiently identifies
  change-points. PDP applied to physical activity data can identify
  transitions from wakefulness to sleep and vice versa. Baek, Jonggyu, Banker,
  Margaret, Jansen, Erica C., She, Xichen, Peterson, Karen E., Pitchford,
  E. Andrew, Song, Peter X. K. (2021) An Efficient Segmentation Algorithm to
  Estimate Sleep Duration from Actigraphy Data <doi:10.1007/s12561-021-09309-3>.