Title: Methods for Fitting and Simulating Non-Stationary ARFIMA Models
Authors@R: person("Benjamin", "Groebe", email = "ben.groebe@gmail.com", role = c("aut", "cre"))
Description: Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.

Description: Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.

Authors@R: c( person("Christian", "Sigg", email =
"christian@sigg-iten.ch", role = c("aut", "cre"), comment =
"ctb", comment = "cancor interface and documentation"))
Description: Two implementations of canonical correlation analysis
(CCA) that are based on iterated regression. By choosing the
appropriate regression algorithm for each data domain, it is
possible to enforce sparsity, non-negativity or other kinds of
constraints on the projection vectors. Multiple canonical
variables are computed sequentially using a generalized
deflation scheme, where the additional correlation not
explained by previous variables is maximized. 'nscancor' is
used to analyze paired data from two domains, and has the same
interface as the 'cancor' function from the 'stats' package
(plus some extra parameters). 'mcancor' is appropriate for
analyzing data from three or more domains. See
Suggests: CCA, glmnet, MASS, PMA, roxygen2, testthat (>= 0.8)
Author: Christian Sigg [aut, cre] (<https://orcid.org/0000-0003-1067-9224>),
R Core team [ctb] (cancor interface and documentation)