Automates documentation of test_that() calls within R test files. 
    The package scans test sources, extracts human-readable test titles (even when 
    composed with functions like paste() or glue::glue(), ... etc.), and generates 
    reproducible roxygen2-style listings that can be inserted both globally and 
    per-section. It ensures idempotent updates and supports customizable numbering 
    templates with hierarchical indices. Designed for developers, QA teams, and package
    maintainers seeking consistent, self-documenting test inventories.

Runs tests using the 'testthat' package but allows for multiple
    attempts for a single test. This is useful for noisy or flaky tests
    that generally pass but can fail due to occasional random errors,
    such as numeric instability or using random data.

Utility functions and 'RStudio' addins for writing,
    running and organizing automated tests. Integrates tightly with the
    packages 'testthat', 'devtools' and 'usethis'.  Hotkeys can be
    assigned to the 'RStudio' addins for running tests in a single file or
    to switch between a source file and the associated test file. In
    addition, testthis provides function to manage and run tests in
    subdirectories of the test/testthat directory.

Tests one hypothesis with several test statistics, correcting for multiple testing.  The central function in the package is testtwice().  In a sensitivity analysis, the method has the largest design sensitivity of its component tests.  The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.

The satisfaction Analysis using the tetraclasse model from Sylvie Llosa. Llosa (1997) <http://www.jstor.org/stable/40592578>.

Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search.

Uses the Distorted Wave Born Approximation (DWBA) to compute the
  acoustic backward scattering, the geometry of the object is formed by a
  volumetric mesh, composed of tetrahedrons. This computation is done efficiently
  through an analytical 3D integration that allows for a solution which is
  expressed in terms of elementary functions for each tetrahedron.
  It is important to note that this method is only valid for objects whose
  acoustic properties, such as density and sound speed, do not vary significantly
  compared to the surrounding medium. (See Lavia, Cascallares and Gonzalez, J. D. (2023). 
  TetraScatt model: Born approximation for the estimation of acoustic dispersion of 
  fluid-like objects of arbitrary geometries. arXiv preprint <arXiv:2312.16721>).