Title: Forecast Verification Routines for the SPECS FP7 Project

Title: Verification Routines for Ensemble Forecasts of Weather and
Authors@R: c(person(given="Stefan", family="Siegert",   role = c("aut", "cre"),
Description: A collection of new forecast verification routines developed for the SPECS
FP7 project. The emphasis is on comparative verification of ensemble forecasts.

Title: Forecast Verification Routines for Ensemble Forecasts of Weather
Description: A collection of forecast verification routines developed for the SPECS
FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.

Title: Spectral Clustering for Mixed Type Data
person("Felix", "Mbuga", role = c("aut")),person("Zander", "Bonnet", role = c("aut")))
Description: Performs cluster analysis of mixed-type data using Spectral Clustering, see F. Mbuga and, C. Tortora (2022) <doi:10.3390/stats5010001>.
Imports: RSpectra, cluster, ggplot2, GGally

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

Packaged: Fri Jul 24 13:18:33 2009; jing

Description: 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.
Packaged: 2016-07-07 14:36:20 UTC; tianhaowu

Title: Versatile Ultra-Fast Spectral Clustering for Single and
Description: 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.
Imports: ggplot2, Rtsne, ClusterR, umap, Rfast, RColorBrewer, diptest
Packaged: 2019-02-04 08:36:33 UTC; christopher