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>).

When using the R package  'exams'  to write mathematics questions in 'Sweave' files, the output of a lot of R functions need to be adjusted for display in mathematical formulas. Specifically, the functions were accumulated when writing questions for the  topics of the mathematics courses College Algebra, Precalculus, Calculus, Differential Equations, Introduction to Probability, and Linear Algebra.  The output of the developed functions can be used in 'Sweave' files.

Checks LaTeX documents and .bib files for typing errors, such as spelling errors, incorrect quotation marks. Also provides useful functions for parsing and linting bibliography files.

Randomizing exams with 'LaTeX'.
    If you can compile your main document with 'LaTeX', the program should be able to compile the randomized
    versions without much extra effort when creating the document.

Statistical extreme value modelling of threshold excesses, maxima
    and multivariate extremes. Univariate models for threshold excesses and maxima
    are the Generalised Pareto, and Generalised Extreme Value model respectively.
    These models may be fitted by using maximum (optionally penalised-)likelihood,
    or Bayesian estimation, and both classes of models may be fitted with covariates
    in any/all model parameters. Model diagnostics support the fitting process.
    Graphical output for visualising fitted models and return level estimates is
    provided. For serially dependent sequences, the intervals declustering algorithm
    of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with
    diagnostic support to aid selection of threshold and declustering horizon.
    Multivariate modelling is performed via the conditional approach of Heffernan
    and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for
    threshold selection and to diagnose estimation convergence.