Efficient methods for computing distance covariance and relevant statistics. See Székely et al.(2007) <doi:10.1214/009053607000000505>; Székely and Rizzo (2013) <doi:10.1016/j.jmva.2013.02.012>; Székely and Rizzo (2014) <doi:10.1214/14-AOS1255>; Huo and Székely (2016) <doi:10.1080/00401706.2015.1054435>.

Computing and plotting the distance covariance and correlation function of a univariate or a multivariate time series. Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included. References include: 
			       a) Edelmann Dominic, Fokianos Konstantinos and Pitsillou Maria (2019). 'An Updated Literature Review of Distance Correlation and Its Applications to Time Series'. International Statistical Review, 87(2): 237--262. <doi:10.1111/insr.12294>.
             b) Fokianos Konstantinos and Pitsillou Maria (2018). 'Testing independence for multivariate time series via the auto-distance correlation matrix'. Biometrika, 105(2): 337--352. <doi:10.1093/biomet/asx082>.
             c) Fokianos Konstantinos and Pitsillou Maria (2017). 'Consistent testing for pairwise dependence in time series'. Technometrics, 59(2): 262--270. <doi:10.1080/00401706.2016.1156024>.
             d) Pitsillou Maria and Fokianos Konstantinos (2016). 'dCovTS: Distance Covariance/Correlation for Time Series'. R Journal, 8(2):324-340. <doi:10.32614/RJ-2016-049>.

Estimates latent variables of public opinion cross-nationally and over time from sparse and incomparable survey data.  'DCPO' uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with 'Stan'.  References: Solt (2020) <doi:10.31235/osf.io/d5n9p>.

Nonparametric smoothing techniques for data on a lattice and
    functional time series. Smoothing is done via kernel regression or
    local polynomial regression, a bandwidth selection procedure based on
    an iterative plug-in algorithm is implemented. This package allows for
    modeling a dependency structure of the error terms of the
    nonparametric regression model.  Methods used in this paper are
    described in Feng/Schaefer (2021)
    <https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021)
    <https://ideas.repec.org/p/pdn/ciepap/143.html>.