c(person(given="Alessandra", family="Cabassi", email = "ac2051@cam.ac.uk", role = c("aut")), person(given="Adam B", family="Kashlak", email = "kashlak@ualberta.ca", role = c("aut", "cre")), person(given="Davide", family="Pigoli", email = "davide.pigoli@kcl.ac.uk", role = c("ctb")))
Alessandra Cabassi [aut],
Adam B Kashlak [aut, cre],
Davide Pigoli [ctb]
Provides a variety of tools for the analysis of covariance operators including k-sample tests for equality and classification and clustering methods found in the works of Cabassi et al (2017) <doi:10.1214/17-EJS1347>, Kashlak et al (2017) <arXiv:1604.06310>, Pigoli et al (2014) <doi:10.1093/biomet/asu008>, and Panaretos et al (2010) <doi:10.1198/jasa.2010.tm09239>.

Fast Implementation of the Diffusion Decision Model
c(person(given="Kendal B.", family="Foster", role=c("aut")),
person(given="Henrik", family="Singmann", role=c("ctb", "cre"),
email="singmann@gmail.com", comment=c(ORCID="0000-0002-4842-3657")))
Provides the probability density function (PDF) of the diffusion decision
model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>)
with across-trial variability in the drift rate.
Because the PDF of the DDM contains an infinite sum, it needs to be
approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009,
<doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014,
<doi:10.1016/j.jmp.2014.05.002>) plus new approximations. All approximations
are implemented purely in 'C++' providing faster speed than existing packages.
rtdists, RWiener, ggplot2, reshape2, testthat, knitr,
rmarkdown, microbenchmark, ggnewscale
Kendal B. Foster [aut],
Henrik Singmann [ctb, cre] (<https://orcid.org/0000-0002-4842-3657>)

rtdists, RWiener, ggplot2, reshape2, testthat, knitr,
rmarkdown, microbenchmark, ggnewscale, ggforce

Provides the probability density function (PDF) and cumulative
distribution function (CDF) of the diffusion decision model
(DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>)
with across-trial variability in the drift rate. Because the PDF and CDF of
the DDM both contain an infinite sum, they needs to be approximated. 'fddm'
implements all published approximations
(Navarro & Fuss, 2009, <doi:10.1016/j.jmp.2009.02.003>;
Gondan, Blurton, & Kesselmeier, 2014, <doi:10.1016/j.jmp.2014.05.002>;
Blurton, Kesselmeier, & Gondan, 2017, <doi:10.1016/j.jmp.2016.11.003>) plus
new approximations. All approximations are implemented purely in 'C++'
providing faster speed than existing packages.