Package: nda 0.1.14

Zsolt T. Kosztyan

nda: Generalized Network-Based Dimensionality Reduction and Analysis

Non-parametric dimensionality reduction function. Reduction with and without feature selection. Plot functions. Automated feature selections. Kosztyan et. al. (2024) <https://doi.org/10.1016/j.eswa.2023.121779>.

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

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nda.pdf |nda.html✨
nda/json (API)
NEWS

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

Peer review:

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

Datasets:
  • COVID19_2020 - Covid'19 case datesets of countries (2020), where the data frame has 138 observations of 18 variables.
  • CWTS_2020 - CWTS Leiden's University Ranking 2020 for all scientific fields, within the period of 2016-2019. 1176 observations (i.e., universities), and 42 variables (i.e., indicators).
  • CrimesUSA1990.X - Crimes in USA cities in 1990. Independent variables
  • CrimesUSA1990.Y - Crimes in USA cities in 1990. Dependent variable
  • GOVDB2020 - Governmental and economic data of countries (2020), where the data frame has 138 observations of 2161 variables.
  • I40_2020 - NUTS2 regional development data (2020) of I4.0 readiness, where the data frame has 414 observations of 101 variables.

On CRAN:

9 exports 2 stars 1.11 score 50 dependencies 1 scripts 279 downloads

Last updated 7 months agofrom:6839740f61. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 11 2024
R-4.5-winNOTESep 11 2024
R-4.5-linuxNOTESep 11 2024
R-4.4-winOKSep 11 2024
R-4.4-macOKSep 11 2024
R-4.3-winOKSep 11 2024
R-4.3-macOKSep 11 2024

Exports:data_gendCordCovfs.dimredfs.KMOndrnormalizepdCorspdCor

Dependencies:base64encbootbslibcachemclicpp11digestenergyevaluatefastmapfontawesomefsglueGPArotationgslhighrhtmltoolshtmlwidgetsigraphjquerylibjsonliteknitrlatticelifecyclemagrittrMASSMatrixmemoisemimemnormtnlmepkgconfigppcorpsychR6rappdirsRcppRcppArmadilloRcppGSLRcppParallelRcppZigguratRfastrlangrmarkdownsasstinytexvctrsvisNetworkxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Package of Generalized Network-based Dimensionality Reduction and Analysesnda-package nda
Biplot function for Generalized Network-based Dimensionality Reduction and Analysis (GNDA)biplot.nda
Covid'19 case datesets of countries (2020), where the data frame has 138 observations of 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's University Ranking 2020 for all scientific fields, within the period of 2016-2019. 1176 observations (i.e., universities), and 42 variables (i.e., indicators).CWTS_2020
Generate random block matrix for GNDAdata_gen
Calculating distance correlation of two vectors or columns of a matrixdCor
Calculating distance covariance of two vectors or columns of a matrixdCov
Feature selection for PCA, FA, and (G)NDAfs.dimred
Feature selection for KMOfs.KMO
Governmental and economic data of countries (2020), where the data frame has 138 observations of 2161 variables.GOVDB2020
NUTS2 regional development data (2020) of I4.0 readiness, where the data frame has 414 observations of 101 variables.I40_2020
Genearlized Network-based Dimensionality Reduction and Analysis (GNDA)ndr
Min-max normalizationnormalize
Calculating partial distance correlation of columns of a matrixpdCor
Plot function for Generalized Network-based Dimensionality Reduction and Analysis (GNDA)plot.nda
Print function of Generalized Network-based Dimensionality Reduction and Analysis (GNDA)print.nda
Calculating semi-partial distance correlation of columns of a matrixspdCor
Summary function of Generalized Network-based Dimensionality Reduction and Analysis (GNDA)summary.nda