Contains a single function named robFitConGraph() which includes two algorithms for robust estimation of scatter matrices subject to zero-constraints in its inverse. The methodology is described in Vogel & Tyler (2014) <doi:10.1093/biomet/asu041>. See robFitConGraph() function documentation for further details.

Contains a function by the same name,
which provides two types of robust t-M-estimators of scatter
subject to zero-constraints in the inverse. The methodology
is described in Vogel & Tyler (2014) <doi:10.1093/biomet/asu041>.
See the robFitConGraph function documentation for further details.
A tutorial including background information is given by
Vogel, Watt & Wiedemann (2022) <arXiv:2204.04291>.

Using Block Model to Estimate the Robustness of Ecological
Network
Implementation of a variety of methods to compute
the robustness of ecological interaction networks with binary interactions
as described in <arXiv:1910.10512>. In particular, using the Stochastic
Block Model and its bipartite counterpart, the Latent Block Model to put a
parametric model on the network, allows the comparison of the robustness of
networks differing in species richness and number of interactions. It also
deals with networks that are partially sampled and/or with missing values.
blockmodels (>= 1.1.2), ggplot2, GREMLINS, igraph, magrittr,
pammtools, partitions, purrr, stats
dplyr, forcats, knitr, patchwork, pbmcapply, RColorBrewer,
rmarkdown, spelling, testthat, tibble, tidyr, tidyverse

Conditionally Unbiased Bounded Influence Estimates
Conditionally unbiased bounded influence estimates as described in
Kuensch et al. (1989) <doi:10.1080/01621459.1989.10478791> in three special cases of
the generalized linear model: Bernoulli, Binomial, and Poisson distributed responses.

Robust pairwise correlations based on estimates of scale,
particularly on "FastQn" one-step M-estimate.

c(person("Sheila", "Goerz", email = "sheila.goerz@tu-dortmund.de", role = c("aut", "cre")), person("Alexander", "Duerre", email = "alexander.duerre@udo.edu", role = "ctb"))
Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. One can detect changes in location, scale and dependence structure of a possibly multivariate time series. Procedures are based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <arXiv:1905.06201>.
Sheila Goerz [aut, cre],
Alexander Duerre [ctb]