c(person("Cristina", "Tortora", role = c("aut", "cre","cph"), email = "grikris1@gmail.com", comment = c(ORCID = "0000-0001-8351-3730")),
person("Felix", "Mbuga", role = c("aut")),person("Zander", "Bonnet", role = c("aut")))
Performs cluster analysis of mixed-type data using Spectral Clustering, see F. Mbuga and, C. Tortora (2022) <doi:10.3390/stats5010001>.
Cristina Tortora [aut, cre, cph]
(<https://orcid.org/0000-0001-8351-3730>),
Felix Mbuga [aut],
Zander Bonnet [aut]

Discovering Genetic Ancestry Using Spectral Graph Theory
Ann Lee, Diana Luca, Bert Klei, Bernie Devlin, Kathryn Roeder

Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps.

Versatile Ultra-Fast Spectral Clustering for Single and
Multi-View Data
A versatile ultra-fast spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in dense regions in the graph. For integrating multi-view data and reducing noise we use a recently developed tensor product graph data integration and diffusion system. 'Spectrum' contains two techniques for finding the number of clusters (K); the classical eigengap method and a novel multimodality gap procedure. The multimodality gap analyses the distribution of the eigenvectors of the graph Laplacian to decide K and tune the kernel. 'Spectrum' is suited for clustering a wide range of complex data.
ggplot2, Rtsne, ClusterR, umap, Rfast, RColorBrewer, diptest

Fast Adaptive Spectral Clustering for Single and Multi-View Data
A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in the graph. For integrating multi-view data and reducing noise a tensor product graph data integration and diffusion procedure is used. 'Spectrum' analyses eigenvector variance or distribution to determine the number of clusters. 'Spectrum' is well suited for a wide range of data, including both Gaussian and non-Gaussian structures.