Model based clustering using 
    the multivariate multiple Scaled t (MST) and multivariate multiple 
    scaled contaminated normal (MSCN) distributions. The MST is an 
    extension of the multivariate Student-t distribution to include 
    flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a  heavy-tailed
    generalization of the multivariate normal (MN) distribution to
    model elliptical contoured scatters in the presence of mild outliers
    (also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.

Three generalizations of the synthetic control method (which has 
    already an implementation in package 'Synth') are implemented: first, 
    'MSCMT' allows for using multiple outcome variables, second, time series 
    can be supplied as economic predictors, and third, a well-defined 
    cross-validation approach can be used.
    Much effort has been taken to make the implementation as stable as possible 
    (including edge cases) without losing computational efficiency.
    A detailed description of the main algorithms is given in 
    Becker and Klößner (2018) <doi:10.1016/j.ecosta.2017.08.002>.

Find common entities detected in both positive and negative
    ionization mode, delete this entity in the less sensible mode and combine both
    matrices.

Multiscale moving sum procedure for the detection of changes in expectation in univariate sequences. References - Multiscale change point detection via gradual bandwidth adjustment in moving sum processes (2021+), Tijana Levajkovic and Michael Messer.

Methods for analyzing and using quartets displayed on a collection 
 of gene trees, primarily to make inferences about the species tree or network 
 under the multi-species coalescent model. These include quartet hypothesis tests
 for the model, as developed by Mitchell et al. (2019) <doi:10.1214/19-EJS1576>, 
 simplex plots of quartet concordance factors as presented by Allman et al. (2020) 
 <doi:10.1101/2020.02.13.948083>, species tree inference methods based on quartet 
 distances of Rhodes (2019) <doi:10.1109/TCBB.2019.2917204> and Yourdkhani and 
 Rhodes (2019) <doi:10.1007/s11538-020-00773-4>, the NANUQ algorithm for inference 
 of level-1 species networks of Allman et al. (2019) <doi:10.1186/s13015-019-0159-2>, 
 the TINNIK algorithm for inference of the tree of blobs of an arbitrary 
 network of Allman et al.(2022) <doi:10.1007/s00285-022-01838-9>, and
 NANUQ+ routines for resolving multifurcations in the tree of blobs to cycles as in
 Allman et al.(2024) (forthcoming). 
 Software announcement by Rhodes et al. (2020) <doi:10.1093/bioinformatics/btaa868>.