Title: Robust Angle Based Joint and Individual Variation Explained
Description: 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>.
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), cowplot, reshape2,

Authors@R: person(given = "William", family = "Kumler",
Description: 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
themselves. This package is designed to format MS data in a tidy fashion and
allows the user perform the plotting and analysis.
Imports: xml2, base64enc, data.table, utils
Suggests: testthat, knitr, rmarkdown, tidyverse, plotly, openxlsx, DBI,

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

Title: Random Projection Kernel Outlier Detector
Description: 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>.
Packaged: 2018-02-05 16:08:39 UTC; CompteZero

Title: Calculate Sketches using Random Projections to Reduce Large Data
Authors@R: c(person("Leo Nils", "Geppert", role = c("aut", "cre", "cph"),
person("Katja", "Ickstadt", role = c("aut")),
person("Alexander", "Munteanu", role = c("aut")),
person("Jens", "Quedenfeld", role = c("aut", "cph")),
person("Ludger", "Sandig", role = c("aut", "cph")),
person("Christian", "Sohler", role = c("aut")))
Description: Calculate sketches of a data set reducing the number of observations using random projections. These can be used for Bayesian or frequentist linear regression on large data sets as described in Geppert et. al (2017) <doi:10.1007/s11222-015-9608-z>.
Author: Leo Nils Geppert [aut, cre, cph],
Maintainer: Leo Nils Geppert <geppert@statistik.uni-dortmund.de>