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.

Create rich and fully interactive timeline visualizations.
    Timelines can be included in Shiny apps or R markdown documents.
    'timevis' includes an extensive API to manipulate a timeline after creation,
    and supports getting data out of the visualization into R. Based on the
    'vis.js' Timeline JavaScript library.

Unleash the power of time-series data visualization with ease using our package. Designed with simplicity 
 in mind, it offers three key features through the 'shiny' package output. The first tab shows time- series 
 charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays 
 Averages per country presented in tables with accompanying sparklines, providing a quick and attractive 
 overview of the data. The last tab presents A customizable world map colored based on user-defined 
 variables for any chosen number of countries, offering an advanced visual approach to understanding 
 geographical data distributions. This package operates with just a few simple arguments, enabling users 
 to conduct sophisticated analyses without the need for complex programming skills. Transform your 
 time-series data analysis experience with our user-friendly tool.