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KODAMA (version 2.2)

Knowledge Discovery by Accuracy Maximization

Description

An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA .

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Version

Install

install.packages('KODAMA')

Monthly Downloads

454

Version

2.2

License

GPL (>= 2)

Maintainer

Stefano Cacciatore

Last Published

September 1st, 2022

Functions in KODAMA (2.2)

lymphoma

Lymphoma Gene Expression Dataset
loads

Variable Ranking
scaling

Scaling Methods
floyd

Find Shortest Paths Between All Nodes in a Graph
frequency_matching

Frequency Matching
txtsummary

Median and Coefficient Interval
normalization

Normalization Methods
pca

Principal Components Analysis
pls.kodama

Partial Least Squares regression.
pls.double.cv

Cross-Validation with PLS-DA.
spirals

Spirals Data Set Generator
swissroll

Swiss Roll Data Set Generator
k.test

K-Test of Statistical Association
transformy

Conversion Classification Vector to Matrix
helicoid

Helicoid Data Set Generator
grid-internal

Internal Grid Functions
KODAMA

Knowledge Discovery by Accuracy Maximization
core_cpp

Maximization of Cross-Validateed Accuracy Methods
mcplot

Evaluation of the Monte Carlo accuracy results
knn.double.cv

Cross-Validation with k-Nearest Neighbors algorithm.
dinisurface

Ulisse Dini Data Set Generator
categorical.test

Categorical Information
MetRef

Nuclear Magnetic Resonance Spectra of Urine Samples
multi_analysis

Continuous Information
correlation.test

Continuous Information
clinical

Clinical Data of a Cohort of Prostate Cancer Patiens
knn.kodama

k-Nearest Neighbors Classifier.
continuous.test

Continuous Information
USA

State of the Union Data Set