Authors@R: 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")))
Maintainer: Adam B Kashlak <kashlak@ualberta.ca>
Description: 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>.
Packaged: 2017-12-21 17:41:21 UTC; alessandra

Title: Fast Implementation of the Diffusion Decision Model
Authors@R: c(person(given="Kendal B.", family="Foster", role=c("aut")),
Description: Provides the probability density function (PDF) of the diffusion decision
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.2014.05.002>) plus new approximations. All approximations
are implemented purely in 'C++' providing faster speed than existing packages.
Suggests: rtdists, RWiener, ggplot2, reshape2, testthat, knitr,
Henrik Singmann [ctb, cre] (<https://orcid.org/0000-0002-4842-3657>)

Description: Provides the probability density function (PDF) and cumulative
distribution function (CDF) of the diffusion decision model
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'
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.

Description: Provides the probability density function (PDF), cumulative
distribution function (CDF), and the partial derivatives of the PDF of the
Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) with across-trial
variability in the drift rate. Because the PDF, its partial derivatives, and
the CDF of the DDM both contain an infinite sum, they need to be approximated.
'fddm' implements all published approximations
rmarkdown, microbenchmark, ggnewscale, ggforce, WienR