This package is considered a duplicate. The official version of this package is found at:https://tkcaccia.r-universe.dev/KODAMA

Package: KODAMA 2.4.1

Stefano Cacciatore

KODAMA: Knowledge Discovery by Accuracy Maximization

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 <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.

Authors:Stefano Cacciatore [aut, trl, cre], Leonardo Tenori [aut]

KODAMA_2.4.1.tar.gz
KODAMA_2.4.1.tar.gz(r-4.5-noble)KODAMA_2.4.1.tar.gz(r-4.4-noble)
KODAMA_2.4.1.tgz(r-4.4-emscripten)KODAMA_2.4.1.tgz(r-4.3-emscripten)
KODAMA.pdf |KODAMA.html✨
KODAMA/json (API)

# Install 'KODAMA' in R:

install.packages('KODAMA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

MetRef - Nuclear Magnetic Resonance Spectra of Urine Samples
USA - State of the Union Data Set
clinical - Clinical Data of a Cohort of Prostate Cancer Patiens
lymphoma - Lymphoma Gene Expression Dataset

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas cpp

4.43 score 2 stars 1 packages 64 scripts 903 downloads 7 mentions 27 exports 21 dependencies

Last updated 2 months agofrom:7b1c47a357. Checks:OK: 1 WARNING: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 06 2024
R-4.5-linux-x86_64	WARNING	Dec 06 2024

Exports:categorical.test continuous.test core_cpp correlation.test dinisurface floyd frequency_matching helicoid k.test knn_Armadillo knn.double.cv knn.kodama KODAMA.matrix KODAMA.visualization loads mcplot multi_analysis normalization pca pls.double.cv pls.kodama RQ scaling spirals swissroll transformy txtsummary

Dependencies:askpass here jsonlite lattice Matrix minerva openssl png rappdirs Rcpp RcppArmadillo RcppEigen RcppTOML reticulate rlang rprojroot RSpectra Rtsne sys umap withr

Knowledge Discovery by Accuracy Maximization

Stefano Cacciatore, Leonardo Tenori

Rendered fromKODAMA.Rmdusingknitr::rmarkdownon Dec 06 2024.

Last update: 2022-12-09
Started: 2016-10-12

Help page	Topics
Categorical Information	categorical.test
Clinical Data of a Cohort of Prostate Cancer Patiens	clinical
Continuous Information	continuous.test
Maximization of Cross-Validateed Accuracy Methods	core_cpp
Continuous Information	correlation.test
Ulisse Dini Data Set Generator	dinisurface
Find Shortest Paths Between All Nodes in a Graph	floyd
Frequency Matching	frequency_matching
Helicoid Data Set Generator	helicoid
K-Test of Statistical Association	k.test
Cross-Validation with k-Nearest Neighbors algorithm.	knn.double.cv
k-Nearest Neighbors Classifier.	knn.kodama
Knowledge Discovery by Accuracy Maximization	KODAMA.matrix
Visualization of KODAMA output	KODAMA.visualization
Variable Ranking	loads
Lymphoma Gene Expression Dataset	lymphoma
Evaluation of the Monte Carlo accuracy results	mcplot
Nuclear Magnetic Resonance Spectra of Urine Samples	MetRef
Continuous Information	multi_analysis
Normalization Methods	normalization
Principal Components Analysis	pca
Cross-Validation with PLS-DA.	pls.double.cv
Partial Least Squares regression.	pls.kodama
Scaling Methods	scaling
Spirals Data Set Generator	spirals
Swiss Roll Data Set Generator	swissroll
Conversion Classification Vector to Matrix	transformy
Median and Coefficient Interval	txtsummary
State of the Union Data Set	USA

Package: KODAMA 2.4.1

KODAMA: Knowledge Discovery by Accuracy Maximization

Knowledge Discovery by Accuracy Maximization

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)