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>.

Implements an efficient algorithm for solving sparse-penalized support vector machines with kernel density convolution. This package is designed for high-dimensional classification tasks, supporting lasso (L1) and elastic-net penalties for sparse feature selection and providing options for tuning kernel bandwidth and penalty weights. The 'dcsvm' is applicable to fields such as bioinformatics, image analysis, and text classification, where high-dimensional data commonly arise. Learn more about the methodology and algorithm at Wang, Zhou, Gu, and Zou (2023) <doi:10.1109/TIT.2022.3222767>.