Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time duration are handled. Both can be regularly or unevenly spaced (overlapping duration are allowed). Only POSIX* format are used for dates and times. The following classes are provided : 'POSIXcti', 'POSIXctp', 'TimeIntervalDataFrame', 'TimeInstantDataFrame', 'SubtimeDataFrame' ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).

Provides a pipeline of tools for analysing circadian 
    time-series data using functional data analysis (FDA). The package 
    supports smoothing of rhythmic time series, functional principle 
    component analysis (FPCA), and extraction of group-level traits from 
    functional representations. Analyses can incorporate multiple curve 
    derivatives and optional temporal segmentation, enabling comparative 
    analysis of circadian dynamics across experimental groups and time 
    windows.

Provides a hypothesis test and variable selection algorithm for use in time-varying, concurrent regression models. The hypothesis test function is also accompanied by a plotting function which will show the estimated beta(s) and confidence band(s) from the hypothesis test. The hypothesis test function helps the user identify significant covariates within the scope of a time-varying concurrent model. The plots will show the amount of area that falls outside the confidence band(s) which is used for the test statistic within the hypothesis test.

Computes how the correlation between 2 time-series changes over time.
    To do so, the package follows the method from Choi & Shin (2021) <doi:10.1007/s42952-020-00073-6>.
    It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy).
    An automatic selection procedure for the bandwidth parameter is implemented.
    Alternative kernels can be used (Epanechnikov, box and normal).
    Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested.