bioc-issue-bot
commented
10 months ago Hi @ManonMartin
Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.
The DESCRIPTION file for this package is:
Package: limpca
Type: Package
Title: An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods
Version: 0.99.0
Authors@R: c(person("Bernadette", "Govaerts", role = c("aut", "ths"),
email = "bernadette.govaerts@uclouvain.be"),
person("Sebastien","Franceschini", role = "ctb",
email="sfranceschini@uliege.be"),
person("Robin","van Oirbeek", role = "ctb",
email="robin.vanoirbeek@gmail.com"),
person("Michel","Thiel", role = "aut",
email="michel.thiel@uclouvain.be"),
person("Pascal","de Tullio", role = "dtc",
email="pdetullio@uliege.be"),
person("Manon","Martin", role = c("aut", "cre"),
email="manon.martin@uclouvain.be",
comment = c(ORCID = "0000-0003-4800-0942")),
person("Nadia", "Benaiche", role = "ctb",
email = "nadia.benaiche@student.uclouvain.be"))
Description: >
This package has for objectives to provide a method to make Linear Models
for high-dimensional designed data. limpca applies a GLM (General Linear Model)
version of ASCA and APCA to analyse multivariate sample profiles generated
by an experimental design. ASCA/APCA provide powerful visualization
tools for multivariate structures in the space of each effect of
the statistical model linked to the experimental design and contrarily
to MANOVA, it can deal with mutlivariate datasets having more variables
than observations. This method can handle unbalanced design.
License: Artistic-2.0
Encoding: UTF-8
LazyData: FALSE
VignetteBuilder: knitr
Imports: ggplot2, stringr, plyr, ggrepel, reshape2,
grDevices, graphics, doParallel, parallel, dplyr, tibble, tidyr, ggsci,
tidyverse, methods, stats
Suggests:
BiocStyle, pander,
rmarkdown, car, gridExtra, knitr
biocViews: StatisticalMethod, PrincipalComponent, Regression, Visualization, ExperimentalDesign, MultipleComparison
RoxygenNote: 7.2.3
Roxygen: list(markdown=TRUE)
BugReports: https://github.com/ManonMartin/limpca/issues
URL: https://github.com/ManonMartin/limpca,
https://manonmartin.github.io/limpca/
lshep
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