SPSL package provides functionality for labeling of
percolation clusters on 2D & 3D square lattice with various
lattice size, relative fraction of accessible sites (occupation
probability), iso- & anisotropy, von Neumann & Moore
(1,d)-neighborhood

Site Percolation on Square Lattices (SPSL)
Provides basic functionality for labeling
iso- & anisotropic percolation clusters on 2D & 3D square lattices
with various lattice sizes, occupation probabilities, von Neumann &
Moore (1,d)-neighborhoods, and random variables weighting
the percolation lattice sites.

Selection by Partitioning the Solution Paths
An implementation of the feature Selection procedure by Partitioning the entire Solution Paths
(namely SPSP) to identify the relevant features rather than using a single tuning parameter.
By utilizing the entire solution paths, this procedure can obtain better selection accuracy than
the commonly used approach of selecting only one tuning parameter based on existing criteria,
cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018)
<doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and
false negative rates) than other variable selection approaches. In addition, it can be flexibly
coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized
estimators.
c(
person(given = "Xiaorui (Jeremy)", family = "Zhu", email = "zhuxiaorui1989@gmail.com", role = c("aut", "cre")),
person(given = "Yang", family = "Liu", email = "yliu23@fhcrc.org", role = c("aut")),
person(given = "Peng", family = "Wang", email = "wangp9@ucmail.uc.edu", role = c("aut"))
)
Xiaorui (Jeremy) Zhu [aut, cre],
Yang Liu [aut],
Peng Wang [aut]

SPatially aUTomatic deNoising for Ims toolKit
Paolo Inglese [aut, cre],
Goncalo Correia [aut, ctb]
c(person("Paolo", "Inglese", email = "p.inglese14@imperial.ac.uk", role = c("aut", "cre")),
person("Goncalo", "Correia", role = c("aut", "ctb")))
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.
spatstat, autothresholdr, viridis, viridisLite, SDMTools,
ggplot2, reshape, imager, methods, infotheo