Import and Handling for Plain and Formatted Text Files (readtext)
Functions for importing and handling text files and formatted text files with additional meta-data, such including ‘.csv’, ‘.tab’, ‘.json’, ‘.xml’, ‘.pdf’, ‘.doc’, ‘.docx’, ‘.xls’, ‘.xlsx’, and others.

Another Look at the Acceptance-Rejection Method (AR)
In mathematics, ‘rejection sampling’ is a basic technique used to generate observations from a distribution. It is also commonly called ‘the Acceptance-Rejection method’ or ‘Accept-Reject algorithm’ and is a type of Monte Carlo method. ‘Acceptance-Rejection method’ is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package ‘AR’ is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package ‘AR’ is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method.

Flexible Modeling of Multivariate Count Data via the Multivariate Conway-Maxwell-Poisson Distribution (multicmp)
A toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.

A Simpler Way to Find Your Files (here)
Constructs paths to your project’s files. The ‘here()’ function uses a reasonable heuristics to find your project’s files, based on the current working directory at the time when the package is loaded. Use it as a drop-in replacement for ‘file.path()’, it will always locate the files relative to your project root.

May All Data be Reproducible and Transparent (MADRaT) * (madrat)
Provides a framework which should improve reproducibility and transparency in data processing. It provides functionality such as automatic meta data creation and management, rudimentary quality management, data caching, work-flow management and data aggregation. * The title is a wish not a promise. By no means we expect this package to deliver everything what is needed to achieve full reproducibility and transparency, but we believe that it supports efforts in this direction.