R: Knowledge Discovery by Accuracy Maximization

# Knowledge Discovery by Accuracy Maximization

## Documentation for package ‘KODAMA’ version 3.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

config.tsne.default | Default configuration for Rtsne
config.umap.default | Default configuration for umap
core_cpp | Maximization of Cross-Validateed Accuracy Methods
dinisurface | Ulisse Dini Data Set Generator
floyd | Find Shortest Paths Between All Nodes in a Graph
helicoid | Helicoid Data Set Generator
kabsch | Kabsch Algorithm
KODAMA.matrix | Knowledge Discovery by Accuracy Maximization
KODAMA.visualization | Visualization of KODAMA output
lymphoma | Lymphoma Gene Expression Dataset
mcplot | Evaluation of the Monte Carlo accuracy results
MDS.defaults | Default configuration for RMDS
MetRef | Nuclear Magnetic Resonance Spectra of Urine Samples
normalization | Normalization Methods
pca | Truncated Principal Components Analysis
scaling | Scaling Methods
spirals | Spirals Data Set Generator
swissroll | Swiss Roll Data Set Generator
transformy | Conversion Classification Vector to Matrix
USA | State of the Union Data Set