spatstat, autothresholdr, viridis, SDMTools, ggplot2, reshape,
imager, methods, infotheo

spatstat, viridis, SDMTools, ggplot2, reshape, imager,
methods, infotheo

A set of tools for the peak filtering of mass spectrometry
imaging data (MSI or IMS) based on spatial distribution of signal. Given a
region-of-interest (ROI), representing the spatial region where the informative
signal is expected to be localized, a series of filters determine which peak
signals are characterized by an implausible spatial distribution. The filters
reduce the dataset dimensionality and increase its information vs noise ratio,
improving the quality of the unsupervised analysis results, reducing data
dimensionality and simplifying the chemical interpretation.
e1071, spatstat, viridis, SDMTools, ggplot2, reshape, imager,
methods, infotheo, parallel

c(person("Paolo", "Inglese", email = "p.inglese@outlook.com", role = c("aut", "cre")),
person("Goncalo", "Correia", role = c("aut", "ctb")))
e1071, edgeR, spatstat, viridis, ggplot2, reshape, imager,
methods, infotheo, parallel, irlba, testthat

e1071, edgeR, spatstat, spatstat.core, spatstat.geom,
spatstat.data, spatstat.sparse, spatstat.utils,
spatstat.linnet, viridis, ggplot2, reshape, imager, methods,
infotheo, parallel, irlba, testthat

e1071, edgeR, spatstat, viridis, ggplot2, reshape, imager,
methods, infotheo, parallel, irlba

e1071, edgeR, spatstat.explore, spatstat.geom, viridis,
ggplot2, reshape, imager, methods, infotheo, parallel, irlba,
doSNOW, foreach

e1071, edgeR, spatstat, spatstat.core, spatstat.geom,
spatstat.data, spatstat.sparse, spatstat.utils,
spatstat.linnet, viridis, ggplot2, reshape, imager, methods,
infotheo, parallel, irlba, testthat, doSNOW, foreach

Data on the Spy vs. Spy comic strip of Mad magazine, created and
written by Antonio Prohias.

Split a Dataset for Training and Testing
c(person("Akhil", "Vakayil", role = c("aut", "cre"), email = "akhilv@gatech.edu"),
person("Roshan", "Joseph", role = c("aut", "ths")),
person("Simon", "Mak", role = "aut"))
Procedure to optimally split a dataset for training and testing.
'SPlit' is based on the method of support points, which is independent of modeling methods.
Please see Joseph and Vakayil (2020) <arXiv:2012.10945> for details.
This work is supported by U.S. National Science Foundation grant CBET-1921873.
Akhil Vakayil [aut, cre],
Roshan Joseph [aut, ths],
Simon Mak [aut]