Package: nda 0.3.0

Zsolt T. Kosztyan

nda: Generalized Network-Based Dimensionality Reduction and Analysis

Constructs interpretable latent variables from communities in squared-association networks. Supports full, partial, and semipartial associations, feature selection, sparse networks, inference, prediction, visualization, and supervised regression. The generalized method is described in Kosztyan et al. (2024) <doi:10.1016/j.eswa.2023.121779>.

Authors:Zsolt T. Kosztyan [aut, cre], Marcell T. Kurbucz [aut], Attila I. Katona [aut], Zahid Khan [aut]

nda_0.3.0.tar.gz
nda_0.3.0.zip(r-4.7-any)nda_0.3.0.zip(r-4.6-any)nda_0.3.0.zip(r-4.5-any)
nda_0.3.0.tgz(r-4.6-any)nda_0.3.0.tgz(r-4.5-any)
nda_0.3.0.tar.gz(r-4.7-any)nda_0.3.0.tar.gz(r-4.6-any)
nda_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
nda/json (API)

# Install 'nda' in R:

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

Bug tracker:https://github.com/kzst/nda/issues

Datasets:

COVID19_2020 - COVID-19 case data for 138 countries and 18 variables
CrimesUSA1990.X - Crimes in USA cities in 1990. Independent variables
CrimesUSA1990.Y - Crimes in USA cities in 1990. Dependent variable
CWTS_2020 - CWTS Leiden University Ranking 2020 with 1,176 universities and 42 indicators
GOVDB2020 - Governmental and economic data for 138 countries and 2,161 variables
I40_2020 - NUTS2 regional Industry 4.0 readiness data with 414 observations and 101 variables

On CRAN:

3.48 score 2 stars 2 scripts 905 downloads 10 exports 46 dependencies

Last updated from:1b692c2a33. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	142
source / vignettes	OK	204
linux-release	OK	116
macos-release	OK	159
macos-oldrel	OK	174
windows-devel	OK	109
windows-release	OK	94
windows-oldrel	OK	87
wasm-release	OK	114

Exports:data_gen dCor dCov fs.dimred fs.KMO ndr ndrlm normalize pdCor spdCor

Dependencies:base64enc boot bslib cachem cli cpp11 digest energy evaluate fastmap fontawesome fs glue GPArotation gsl highr htmltools htmlwidgets igraph jquerylib jsonlite knitr lattice lifecycle magrittr MASS Matrix mco memoise mime mnormt nlme pkgconfig ppcor psych R6 rappdirs Rcpp rlang rmarkdown sass tinytex vctrs visNetwork xfun yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Package of Generalized Network-based Dimensionality Reduction and Analyses	nda-package nda
Biplot NDA Scores and Loadings	biplot.nda
COVID-19 case data for 138 countries and 18 variables	COVID19_2020
Crimes in USA cities in 1990. Independent variables (X)	CrimesUSA1990.X
Crimes in USA cities in 1990. Dependent variable (Y)	CrimesUSA1990.Y
CWTS Leiden University Ranking 2020 with 1,176 universities and 42 indicators	CWTS_2020
Generate Structured NDA Example Data	data_gen
Distance-Correlation Matrix	dCor
Distance-Covariance Matrix	dCov
NDRLM Fitted Values	fitted.ndrlm
Loading-Based Feature Selection	fs.dimred
KMO-Based Feature Selection	fs.KMO
Governmental and economic data for 138 countries and 2,161 variables	GOVDB2020
NUTS2 regional Industry 4.0 readiness data with 414 observations and 101 variables	I40_2020
Generalized Network-Based Dimensionality Reduction and Analysis	ndr
Network-Based Dimensionality Reduction and Regression	ndrlm
Fast Min-Max Normalization	normalize
Partial Distance-Correlation Matrix	pdCor
Plot an NDA Association Network	plot.nda
Plot an NDRLM Path Network	plot.ndrlm
Predict NDA Scores	predict.nda
Predict from an NDRLM Model	predict.ndrlm
Print an NDA Model	print.nda
Print an NDRLM Model	print.ndrlm
NDRLM Residuals	residuals.ndrlm
Semipartial Distance-Correlation Matrix	spdCor
Summarize an NDA Model	summary.nda
Summarize an NDRLM Model	summary.ndrlm