Robust Angle Based Joint and Individual Variation Explained
c(
person(given = "Erica",
family = "Ponzi",
role = c("aut", "cre"),
email = "erica.ponzi@medisin.uio.no"),
person(given = "Abhik",
family = "Ghosh",
role = c("aut"),
email = "abhik.ghosh.stat@gmail.com"))
A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.
knitr, rmarkdown, testthat (>= 2.1.0), cowplot, reshape2,
dplyr
Erica Ponzi [aut, cre],
Abhik Ghosh [aut]

person(given = "William", family = "Kumler",
email = "wkumler@uw.edu",
role = c("aut", "cre", "cph"))
R-based access to mass-spectrometry (MS) data. While many packages
exist to process MS data, many of these make it difficult to
access the underlying mass-to-charge ratio (m/z), intensity, and
retention time of the files
themselves. This package is designed to format MS data in a tidy fashion and
allows the user perform the plotting and analysis.
testthat, knitr, rmarkdown, tidyverse, plotly, openxlsx, DBI,
RSQLite, reticulate

c(
person(given = "William", family = "Kumler", email = "wkumler@uw.edu",
role = c("aut", "cre", "cph")),
person(given = "Ricardo", family = "Cunha", role = "ctb"),
person(given = "Ethan", family = "Bass", role = "ctb")
)
testthat, knitr, rmarkdown, tidyverse, ggplot2, dplyr,
plotly, openxlsx, DBI, RSQLite, reticulate
William Kumler [aut, cre, cph],
Ricardo Cunha [ctb],
Ethan Bass [ctb]

Random Projection Kernel Outlier Detector
Kernel method that performs online outlier detection through random low-dimensional projections in a kernel space. Controls the probability of false alarm error. See Kellner J., "Gaussian models and kernel methods" (2016), PhD thesis for reference <https://ori-nuxeo.univ-lille1.fr/nuxeo/site/esupversions/789d119a-6763-4205-972f-b318cd4fdb27>.