Estimation of Multivariate Long-Memory Models Parameters
c(person("Sophie", "Achard", role=c("aut","cre"), email="sophie.achard@gipsa-lab.fr"),person("Irene","Gannaz", role=c("aut"), email="irene.gannaz@insa-lyon.fr"))
Computation of an estimation of the long-memory parameters and
the long-run covariance matrix using a multivariate model
(Lobato, 1999; Shimotsu 2007). Two semi-parametric methods are
implemented: a Fourier based approach (Shimotsu 2007) and a wavelet based
approach (Achard and Gannaz 2014).
Sophie Achard [aut, cre],
Irene Gannaz [aut]

Computation of an estimation of the long-memory parameters and
the long-run covariance matrix using a multivariate model
(Lobato (1999) <10.1016/S0304-4076(98)00038-4>; Shimotsu (2007) <10.1016/j.jeconom.2006.01.003>). Two semi-parametric methods are
implemented: a Fourier based approach (Shimotsu (2007) <10.1016/j.jeconom.2006.01.003>) and a wavelet based
approach (Achard and Gannaz (2016) <10.1111/jtsa.12170>).

Computation of an estimation of the long-memory parameters and
the long-run covariance matrix using a multivariate model
(Lobato (1999) <doi:10.1016/S0304-4076(98)00038-4>; Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>). Two semi-parametric methods are
implemented: a Fourier based approach (Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>) and a wavelet based
approach (Achard and Gannaz (2016) <doi:10.1111/jtsa.12170>).

Fits multi-way component models via alternating least squares algorithms with optional constraints (orthogonality and non-negativity). Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.

Fits multi-way component models via alternating least squares algorithms with optional constraints (orthogonality, non-negativity, and structural). Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.

Fits multi-way component models via alternating least squares algorithms with optional constraints: orthogonal, non-negative, unimodal, monotonic, periodic, smooth, or structure. Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.