A set of functions allowing to implement the 'SpiceFP' approach
which is iterative. It involves transformation of functional
predictors into several candidate explanatory matrices (based on contingency
tables), to which relative edge matrices with contiguity constraints are
associated. Generalized Fused Lasso regression are performed in order to
identify the best candidate matrix, the best class intervals and related
coefficients at each iteration. The approach is stopped when the maximal number
of iterations is reached or when retained coefficients are zeros. Supplementary
functions allow to get coefficients of any candidate matrix or mean of
coefficients of many candidates.
doParallel, foreach, stringr, tidyr, Matrix, genlasso, purrr

A user-friendly tool for estimating both total and directional volatility spillovers based on Diebold and Yilmaz (2009, 2012). It also provides the user with rolling estimation for total and net indices. User can find both orthogonalized and generalized versions for each kind of measures. See Diebold and Yilmaz (2009, 2012) find them at  <doi:10.1111/j.1468-0297.2008.02208.x> and <doi:10.1016/j.ijforecast.2011.02.006>.

Spillover/Connectedness Index Based on VAR Modelling
A user-friendly tool for estimating both total and directional connectedness spillovers based on Diebold and Yilmaz (2009, 2012). It also provides the user with rolling estimation for total and net indices. User can find both orthogonalized and generalized versions for each kind of measures. See Diebold and Yilmaz (2009, 2012) find them at  <doi:10.1111/j.1468-0297.2008.02208.x> and <doi:10.1016/j.ijforecast.2011.02.006>.

Functional Data Analysis using Splines and Orthogonal Spline
Bases
c(person("Xijia", "Liu", role = "aut",email = "xijia.liu@umu.se"), person("Krzysztof", "Podgorski", role = c("aut","cre","cph"), email = "Krzysztof.Podgorski@stat.lu.se",) )
Splines are efficiently represented through their Taylor expansion at the knots. The representation accounts for the support sets and is thus suitable for sparse functional data. The B-splines and orthogonal bases of splines that reside on small total support are implemented. The orthogonal bases and are utilized for functional data analysis. Random spline generator is implemented as well as all fundamental algebraic and calculus operations on splines. The optimal, in the least square sense, functional fit to data consisting of sampled values of functions as well as splines build over another set of knots is obtained. Podgórski, K. (2021)  <arXiv:2102.00733>.
Xijia Liu [aut],
Krzysztof Podgorski [aut, cre, cph]