Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) <doi:10.3150/16-BEJ887> and the multiscale algorithmic extension from Cho and Kirch (2022) <doi:10.1007/s10463-021-00811-5>, as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) <doi:10.1016/j.csda.2022.107552>. See also Meier, Kirch and Cho (2021) <doi:10.18637/jss.v097.i08> which accompanies the R package.
methods, RColorBrewer, plot3D, Rcpp (>= 0.12.5)

Detect nonlinear functional relations. This package was originally
designed for the identifiability analysis of nonlinear dynamical models. However,
the concept is very general and allows to detect groups of functionally related
variables whenever there are multiple estimates for each variable, which can be written
in matrix K. The essence of the algorithm is the calculation of mean optimal
transformations estimated from bootstrap samples drawn from K.

c(
person(given = "Jakub", family = "Nowosad",
role = c("aut", "cre"),
email = "nowosad.jakub@gmail.com",
comment = c(ORCID = "0000-0002-1057-3721")))
Describes spatial patterns of categorical raster data for
any defined regular and irregular areas.
Patterns are described quantitatively using built-in signatures
based on co-occurrence matrices but also allows for
any user-defined functions.
It enables spatial analysis such as search, change detection,
and clustering to be performed on spatial patterns (Nowosad (2021) <doi:10.1007/s10980-020-01135-0>).
comat, philentropy, Rcpp, sf, stars, tibble
covr, dplyr, spdep, knitr, rmarkdown, testthat (>= 2.1.0)

covr, dplyr, spdep, knitr, rmarkdown, testthat (>= 2.1.0),
terra

comat, philentropy (>= 0.6.0), Rcpp, sf, stars, tibble

Motif-Based Spectral Clustering of Weighted Directed Networks
person(given = "William George",
family = "Underwood",
role = c("aut", "cre"),
email = "wgu2@princeton.edu")
Tools for spectral clustering of weighted directed networks using motif
adjacency matrices. Methods perform well on large and sparse networks, and
random sampling methods for generating weighted directed networks are also
provided. Based on methodology detailed in Underwood, Elliott and Cucuringu
(2020) <arXiv:2004.01293>.