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.

Streaming 'JSON' ('ndjson') has one 'JSON' record per-line
        and many modern 'ndjson' files contain large numbers of records.
        These constructs may not be columnar in nature, but it is often
        useful to read in these files and "flatten" the structure out to
        enable working with the data in an R 'data.frame'-like context.
        Functions are provided that make it possible to read in plain
        'ndjson' files or compressed ('gz') 'ndjson' files and either
        validate the format of the records or create "flat" 'data.table'
        structures from them.