doParallel, knitr, parallelDist, testthat, TSclust, TSdist

parallel, stats, utils, clue, cluster, dplyr, flexclust,
foreach, ggplot2, ggrepel, Matrix, RSpectra, Rcpp, RcppParallel
(>= 4.4.0), reshape2, shiny, shinyjs

parallel, stats, utils, clue, cluster, dplyr, flexclust,
foreach, ggplot2, ggrepel, Matrix (>= 1.5-0), RSpectra, Rcpp,
RcppParallel (>= 4.4.0), reshape2, shiny, shinyjs

Automatic Differentiation with Dual Numbers
c(person("Luca", "Sartore",
role = c("aut", "cre"),
email = "drwolf85@gmail.com",
comment = c(ORCID = "0000-0002-0446-1328")))
Automatic differentiation is achieved by using dual numbers without
providing hand-coded gradient functions. The output value of a mathematical
function is returned with the values of its exact first derivative
(or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind
(2018) <http://jmlr.org/papers/volume18/17-468/17-468.pdf>.

Automatic differentiation is achieved by using dual numbers without
providing hand-coded gradient functions. The output value of a mathematical
function is returned with the values of its exact first derivative
(or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind
(2018) <https://jmlr.org/papers/volume18/17-468/17-468.pdf>.

Dual Sparse Partial Least Squares Regression
c(person(given = "Louna",
family = "Alsouki",
role = c("aut", "cre"),
email = "lounasouki@gmail.com"),
person(given = "François",
family = "Wahl",
role = c("aut"),
email = "wahl@math.univ-lyon1.fr"),
person(given = "Ghislain",
family = "Durif",
role = c("ctb"),
email = "ghislain.durif@umontpellier.fr")
)
Provides a series of functions for fitting a dual sparse partial least squares (Dual-SPLS) regression. These functions differ by the choice of the underlying norm.
Louna Alsouki [aut, cre],
François Wahl [aut],
Ghislain Durif [ctb]

Dual Scaling Analysis of Multiple Choice Data
Jose G. Clavel, Shizuiko Nishisato and Antonio Pita
Functions to analyze multiple choice data using Dual Scaling

Decimated and Undecimated 2D Complex Dual-Tree Wavelet Transform
c( person("Sebastian", "Buschow", email = "s6sebusc@uni-bonn.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4750-361X")),
person("Nick", "Kingsbury", role = c("ctb")),
person("Rich", "Wareham", role = c("ctb"))
)
An implementation of the decimated two-dimensional complex dual-tree wavelet transform as described in Kingsbury (1999) <doi:10.1098/rsta.1999.0447> and Selesnick et al. (2005) <doi:10.1109/MSP.2005.1550194>. Also includes the undecimated version and spectral bias correction described in Nelson et al. (2018) <doi:10.1007/s11222-017-9784-0>. The code is partly based on the 'dtcwt' Python library.
Sebastian Buschow [aut, cre] (<https://orcid.org/0000-0003-4750-361X>),
Nick Kingsbury [ctb],
Rich Wareham [ctb]