Implementations for several robust procedures that allow for (online)
        extraction of the signal of univariate or multivariate time series by
        applying robust regression techniques to a moving time window are provided.
        Included are univariate filtering procedures based on repeated-median 
        regression as well as hybrid and trimmed filters derived from it; 
        see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive 
        online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> 
        and the slope comparing adaptive repeated median by Borowski and Fried (2013) 
        <doi:10.1007/s11222-013-9391-7> choose the width of the moving time 
        window adaptively. Multivariate versions are also provided; see  
        Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate 
        online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393>  
        for a multivariate slope comparing adaptive repeated median. Furthermore, 
        a repeated-median based filter with automatic outlier replacement and 
        shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.

Functions for implementing robust methods for functional linear regression. In the functional linear regression, we consider scalar-on-function linear regression and function-on-function linear regression. More details, see Beyaztas, U., and Shang, H. L. (2021) <arXiv:2111.01238> and Beyaztas, U., and Shang, H. L. (2022) <arXiv:2203.05065>.

Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) <DOI:10.1080/00949655.2017.1359601>.

Assesses the robustness of the community structure of a network found by one or more community detection algorithm to give indications about their reliability. It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) <https://journal.r-project.org/archive/2021/RJ-2021-040/index.html>.