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.

Provides tools for fitting the neutral community model (NCM) to 
    assess the role of stochastic processes in community assembly. The package 
    implements the framework of Sloan et al. (2006) <doi:10.1111/j.1462-2920.2005.00956.x>,
    enabling users to evaluate neutral dynamics in ecological and microbial communities.

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.

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/>.