| nda-package | Package of Generalized Network-based Dimensionality Reduction and Analyses |
| biplot.nda | Biplot function for Generalized Network-based Dimensionality Reduction and Analysis (GNDA) |
| COVID19_2020 | Covid'19 case datesets of countries (2020), where the data frame has 138 observations of 18 variables. |
| CrimesUSA1990.X | Crimes in USA cities in 1990. Independent variables (X) |
| CrimesUSA1990.Y | Crimes in USA cities in 1990. Dependent variable (Y) |
| 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). |
| data_gen | Generate random block matrix for GNDA |
| dCor | Calculating distance correlation of two vectors or columns of a matrix |
| dCov | Calculating distance covariance of two vectors or columns of a matrix |
| fs.dimred | Feature selection for PCA, FA, and (G)NDA |
| fs.KMO | Feature selection for KMO |
| 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. |
| nda | Package of Generalized Network-based Dimensionality Reduction and Analyses |
| ndr | Genearlized Network-based Dimensionality Reduction and Analysis (GNDA) |
| normalize | Min-max normalization |
| pdCor | Calculating partial distance correlation of columns of a matrix |
| plot.nda | Plot function for Generalized Network-based Dimensionality Reduction and Analysis (GNDA) |
| spdCor | Calculating semi-partial distance correlation of columns of a matrix |
| summary.nda | Summary function of Generalized Network-based Dimensionality Reduction and Analysis (GNDA) |