Allows to generate automatically 'testthat' code files from offensive 
    programming test cases. Generated test files are complete and ready to run.
    Using 'wyz.code.testthat' you will earn a lot of time, reduce the number of
    errors in test case production, be able to test immediately generated files 
    without any need to view or modify them, and enter a zero time latency between 
    code implementation and industrial testing. As with 'testthat', you may
    complete provided test cases according to your needs to push testing further, 
    but this need is nearly void when using 'wyz.code.offensiveProgramming'.

The 'X13-ARIMA-SEATS' <https://www.census.gov/data/software/x13as.html> methodology and software is a widely used software and developed by the US Census Bureau. It can be accessed from 'R' with this package and 'X13-ARIMA-SEATS' binaries are provided by the 'R' package 'x13binary'.

The US Census Bureau provides a seasonal adjustment program now
 called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and
 X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau
 offers both source and binary versions -- which this package integrates for
 use by other R packages.

The x3p file format  is specified in ISO standard 5436:2000 to 
    describe 3d surface measurements. 'x3ptools' allows reading, writing and 
    basic modifications to the 3D surface measurements.

We consider the problem where we observe k vectors (possibly of different lengths), each representing an independent multinomial random vector. For a given function that takes in the concatenated vector of multinomial probabilities and outputs a real number, this is a Monte Carlo estimation procedure of an exact p-value and confidence interval. The resulting inference is valid even in small samples, when the parameter is on the boundary, and when the function is not differentiable at the parameter value, all situations where asymptotic methods and the bootstrap would fail. For more details see Sachs, Fay, and Gabriel (2025) <doi:10.48550/arXiv.2406.19141>.