Provides functionality for performing Nearest Centroid (NC) Sampling. The NC sampling procedure was developed for forestry applications and selects plots for ground measurement so as to maximize the efficiency of imputation estimates. It uses multiple auxiliary variables and multivariate clustering to search for an optimal sample. Further details are given in Melville G. & Stone C. (2016) <doi:10.1080/00049158.2016.1218265>.

Inference and dependence measure for the non-central squared Gaussian, Student, Clayton, Gumbel, and Frank copula models.The description of the methodology is taken from Section 3 of Nasri, Remillard and Bouezmarni (2019) <doi:10.1016/j.jmva.2019.03.007>.

Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more.
    'NCutYX' allows clustering of variables, of samples, and both variables and samples (biclustering),
    while incorporating the dependencies across multiple types of Omics data.
    (SJ Teran Hidalgo et al (2017), <doi:10.1186/s12864-017-3990-1>).

Fits regularization paths for linear regression, GLM, and Cox
  regression models using lasso or nonconvex penalties, in particular the
  minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)
  penalty, with options for additional L2 penalties (the "elastic net" idea).
  Utilities for carrying out cross-validation as well as post-fitting
  visualization, summarization, inference, and prediction are also provided.
  For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388>
  or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.

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

Computes various geospatial indices of socioeconomic deprivation and disparity
             in the United States. Some indices are considered "spatial" because they 
             consider the values of neighboring (i.e., adjacent) census geographies in
             their computation, while other indices are "aspatial" because they only 
             consider the value within each census geography. Two types of aspatial 
             neighborhood deprivation indices (NDI) are available: including:
             (1) based on Messer et al. (2006) <doi:10.1007/s11524-006-9094-x>
             and (2) based on Andrews et al. (2020) <doi:10.1080/17445647.2020.1750066> 
             and Slotman et al. (2022) <doi:10.1016/j.dib.2022.108002>
             who use variables chosen by Roux and Mair (2010)
             <doi:10.1111/j.1749-6632.2009.05333.x>. Both are a decomposition of multiple
             demographic characteristics from the U.S. Census Bureau American Community 
             Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5
             (2005-2009 onward), the package can also compute indices of racial or ethnic
             residential segregation, including but limited to those discussed in Massey 
             & Denton (1988) <doi:10.1093/sf/67.2.281>, and additional indices of 
             socioeconomic disparity.