Shrinks a failing R script to the smallest subset of statements
    that still triggers the same error, using the delta debugging algorithm of
    Zeller and Hildebrandt (2002) <doi:10.1109/32.988498>. Each candidate
    reduction is evaluated in a separate R process, so dependencies between
    statements and their side effects are respected. The result is a
    one-minimal example, in which removing any remaining statement makes the
    error disappear; this is the form most useful for bug reports and for
    questions on community forums. A general delta debugging routine and a
    helper for reducing data frames to the rows that reproduce a failure are
    also provided.

In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum  number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. For method details see, Bhowmik, A.,Varghese, E., Jaggi, S. and Varghese, C. (2017)<doi:10.1080/03610926.2016.1152490>.This package used to construct all possible minimally changed factorial run orders for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function minFactDesign().

Provides tools for creating agents with persistent state using 
    R6 classes <https://cran.r-project.org/package=R6> and the 'ellmer' 
    package <https://cran.r-project.org/package=ellmer>. Tracks prompts, 
    messages, and agent metadata for reproducible, multi-turn large language 
    model sessions.