Spatial (cross-)covariance and related geostatistical tools: the
        nonparametric (cross-)covariance function , the spline correlogram, the
        nonparametric phase coherence function, local indicators of spatial 
        association (LISA), (Mantel) correlogram, (Partial) Mantel test.

Extract metadata from 'NetCDF' data sources, these can be files, file handles or
 servers. This package leverages and extends the lower level functions of the 'RNetCDF' package 
 providing a consistent set of functions that all return data frames. We introduce named concepts 
 of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 
 'NetCDF' library <https://www.unidata.ucar.edu/software/netcdf/>. 'RNetCDF' matches the library 
 itself with only the named concepts of 'variables', 'dimensions' and 'attributes'.

A set of handy functions. Includes a versatile one line progress bar, one 
 line function timer with detailed output, time delay function, text histogram, object 
 preview, CRAN package search, simpler package installer, Linux command install check, 
 a flexible Mode function, top function, simulation of correlated data, and more.

A set of techniques that can be used to develop, validate, and implement automated classifiers. A powerful tool for transforming raw data into meaningful information, 'ncodeR' (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is designed specifically for working with big data: large document collections, logfiles, and other text data.

An efficient unified nonconvex penalized estimation algorithm for
    Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit,
    and Cox proportional hazard regression models.
    The unified algorithm is implemented based on the convex concave procedure and
    the algorithm can be applied to most of the existing nonconvex penalties.
    The algorithm also supports convex penalty:
    least absolute shrinkage and selection operator (LASSO).
    Supported nonconvex penalties include
    smoothly clipped absolute deviation (SCAD),
    minimax concave penalty (MCP), truncated LASSO penalty (TLP),
    clipped LASSO (CLASSO), sparse ridge (SRIDGE),
    modified bridge (MBRIDGE) and modified log (MLOG).
    For high-dimensional data (data set with many variables),
    the algorithm selects relevant variables producing a parsimonious regression model.
    Kim, D., Lee, S. and Kwon, S. (2018) <arXiv:1811.05061>,
    Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>,
    Kwon, S., Lee, S. and Kim, Y. (2015) <doi:10.1016/j.csda.2015.07.001>.
    (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine
    Graziadio Business School and the National Research Foundation of Korea.)