Provides the ability to generate images from documents of
    different types. Three main features are provided: generating document
    thumbnails, performing visual tests of documents, and updating fields
    and tables of contents of a 'Microsoft Word' or 'RTF' document.
    'Microsoft Word' and/or 'LibreOffice' must be installed on the
    machine. If 'Microsoft Word' is available, it can produce PDF
    documents or images identical to the originals; otherwise
    'LibreOffice' is used and the rendering may sometimes differ from the
    original documents.

Define a command-line interface by just giving it
    a description in the specific format.

A direct approach to optimal designs for copula models based on
    the Fisher information. Provides flexible functions for building joint PDFs,
    evaluating the Fisher information and finding optimal designs. It includes an
    extensible solution to summation and integration called 'nint', functions for
    transforming, plotting and comparing designs, as well as a set of tools for
    common low-level tasks.

A framework for creating production outputs. Users can frame a table, listing, or figure with headers and footers and save to an output file. Stores an intermediate 'docorator' object for reproducibility and rendering to multiple output types.

Basic routines used in scientific coding, such as timing routines, vector/array handing functions and I/O support routines.

Distributed Online Covariance Matrix Tests 'Docovt' is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform covariance matrix tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, 'Docovt' ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for covariance matrix estimation and hypothesis testing. The philosophy of 'Docovt' is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.