c(
person(
"Robrecht", "Cannoodt",
email = "rcannood@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-3641-729X", github = "rcannood")
),
person(
"Wouter", "Saelens",
email = "wouter.saelens@ugent.be",
role = c("ctb"),
comment = c(ORCID = "0000-0002-7114-6248", github = "zouter")
)
)
dplyr, dynutils (>= 1.0.3), dyndimred (>= 1.0.1), grDevices,
ggplot2 (>= 2.0), MASS, Matrix, mclust, pbapply, pheatmap,
princurve (>= 2.1.3), purrr, ranger, RColorBrewer, reshape2,
stats, tidyr, TSP, utils
Robrecht Cannoodt [aut, cre] (<https://orcid.org/0000-0003-3641-729X>,
rcannood),
Wouter Saelens [ctb] (<https://orcid.org/0000-0002-7114-6248>, zouter)

An accurate and easy tool for performing linear trajectory inference on
single cells using single-cell RNA sequencing data. In addition, SCORPIUS
provides functions for discovering the most important genes with respect to
the reconstructed trajectory, as well as nice visualisation tools.
Cannoodt et al. (2016) <doi:10.1101/079509>.
dplyr, dynutils (>= 1.0.3), dyndimred (>= 1.0.1), grDevices,
ggplot2 (>= 2.0), MASS, Matrix, mclust, pbapply, pheatmap,
princurve (>= 2.1.4), purrr, ranger, RColorBrewer, stats,
tidyr, TSP, utils

dplyr, dynutils (>= 1.0.3), dynwrap, grDevices, ggplot2 (>=
2.0), lmds, MASS, Matrix, mclust, methods, pbapply, pheatmap,
princurve (>= 2.1.4), purrr, ranger, RANN, RColorBrewer, stats,
tidyr, TSP

knitr, rmarkdown, R.rsp, testthat (>= 2.1.0)

dplyr, dynutils (>= 1.0.3), dynwrap, grDevices, ggplot2 (>=
2.0), lmds, MASS, Matrix, mclust, methods, pbapply, pheatmap,
princurve (>= 2.1.4), purrr, ranger, RANN, RColorBrewer,
reshape2, stats, tidyr, TSP

covr, knitr, rmarkdown, R.rsp, testthat (>= 2.1.0)

c(
person("Robrecht", "Cannoodt", email = "robrecht.cannoodt@gmail.com", role = c("aut", "cre")))
dplyr, fitdistrplus, grDevices, ggplot2 (>= 2.0), magrittr,
MASS, mclust, Rcpp, pbapply, pheatmap, princurve, purrr,
ranger, RColorBrewer, reshape2, splines, stats, testthat,
tidyr, TSP, utils

c(person(given = "Alba",
family = "Gonzalez Cebrian",
role = c("aut", "cre"),
email = "algonceb@upv.es"),
person(given = "Abel",
family = "Folch-Fortuny",
role = "aut",
email = "Abel.Folch-Fortuny@dsm.com"),
person(given = "Francisco",
family = "Arteaga",
role = c("aut"),
email = "francisco.arteaga@ucv.es"),
person(given = "Alberto",
family = "Ferrer",
role = "aut",
email = "aferrer@eio.upv.es"))
Using principal component analysis as a base model, 'SCOUTer'
offers a new approach to simulate outliers in a simple and precise way.
The user can generate new observations defining them by a pair of well-known
statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2)
statistics. Just by introducing the target values of the SPE and T^2, 'SCOUTer'
returns a new set of observations with the desired target properties.
Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and
Alberto Ferrer (2020).
Alba Gonzalez Cebrian [aut, cre],
Abel Folch-Fortuny [aut],
Francisco Arteaga [aut],
Alberto Ferrer [aut]