Helps with the thoughtful saving, reading, and management of result
  files (using 'rds' files). The core functions take a list of parameters that are
  used to generate a unique hash to save results under. Then, the same
  parameter list can be used to read those results back in. This
  is helpful to avoid clunky file naming when running a large number of
  simulations. Additionally, helper functions are available for compiling a flat
  file of parameters of saved results, monitoring result usage, and cleaning up
  unwanted or unused results. For more information, visit the 'indexr' homepage 
  <https://lharris421.github.io/indexr/>.

Quick indexation of any type of vector or of any combination of those. Indexation turns a vector into an integer vector going from 1 to the number of unique elements. Indexes are important building blocks for many algorithms. The method is described at <https://github.com/lrberge/indexthis/>.

Allows the construction selection indices based on estimated breeding values in animal and plant breeding and to calculate several analytic measures around to assess its impact on genetic and phenotypic progress. The methodology thereby allows to analyze genetic gain of traits in the breeding goal which are not part of the actual index and automatically computes several analytic measures. It further allows to retrospectively derive realized economic weights from observed genetic trends. The framework is described in Simianer, H., Heise, J., Rensing, S., Pook, T. Geibel, J. and Reimer, C. (2023) <doi:10.1186/s12711-023-00807-0>.

Computation of test statistics of independence between  (continuous) innovations of time series. They can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) <doi:10.1002/cjs.11141>.

Set of routines for influence diagnostics by using case-deletion in ordinary least 
    squares, nonlinear regression [Ross (1987). <doi:10.2307/3315198>], ridge estimation [Walker and Birch (1988). <doi:10.1080/00401706.1988.10488370>] 
    and least absolute deviations (LAD) regression [Sun and Wei (2004). <doi:10.1016/j.spl.2003.08.018>].