Designed for studies where animals tagged with acoustic tags are expected
    to move through receiver arrays. This package combines the advantages of automatic sorting and checking
    of animal movements with the possibility for user intervention on tags that deviate from expected
    behaviour. The three analysis functions (explore(), migration() and residency())
    allow the users to analyse their data in a systematic way, making it easy to compare results from
    different studies.
    CJS calculations are based on Perry et al. (2012) <https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>.

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>.