Flexible general-purpose toolbox implementing genetic 
  algorithms (GAs) for stochastic optimisation. Binary, real-valued, and
  permutation representations are available to optimize a fitness 
  function, i.e. a function provided by users depending on their 
  objective function. Several genetic operators are available and can be
  combined to explore the best settings for the current task. 
  Furthermore, users can define new genetic operators and easily 
  evaluate their performances. Local search using general-purpose 
  optimisation algorithms can be applied stochastically to exploit 
  interesting regions. GAs can be run sequentially or in parallel, using
  an explicit master-slave parallelisation or a coarse-grain islands 
  approach. For more details see Scrucca (2013) 
  <doi:10.18637/jss.v053.i04> and Scrucca (2017) 
  <doi:10.32614/RJ-2017-008>.

This is a companion to Henry-Stewart talk by Zhao (2026, <doi:10.69645/FRFQ9519>),
 which gathers information, metadata and scripts to showcase modern genetic analysis -- ranging from testing
 of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with traits using genetic and statistical
 models, Bayesian implementation, power calculation in study design, and genetic annotation. It also covers
 R integration with the Linux environment, GitHub, package creation and web applications. The earlier version
 by Zhao (2009, <doi:10.69645/DCRY5578>) provides a brief introduction to these topics.

Help to the occasional R user for synthesis and enhanced graphical visualization of redundancy analysis (RDA) and principal component analysis (PCA) methods and objects.
  Inputs are : data frame, RDA (package 'vegan') and PCA (package 'FactoMineR') objects.
  Outputs are : synthesized results of RDA, displayed in console and saved in tables ; displayed and saved objects of PCA graphic visualization of individuals and variables projections with multiple graphic parameters.

Provides deterministic forecasting for weekly, monthly, quarterly, and yearly
    time series using the Generalized Adaptive Capped Estimator. The method
    includes preprocessing for missing and extreme values, extraction of multiple
    growth components (including long-term, short-term, rolling, and drift-based
    signals), volatility-aware asymmetric capping, optional seasonal adjustment
    via damped and normalized seasonal factors, and a recursive forecast
    formulation with moderated growth. The package includes a user-facing
    forecasting interface and a plotting helper for visualization. Related
    forecasting background is discussed in Hyndman and Athanasopoulos (2021)
    <https://otexts.com/fpp3/> and Hyndman and Khandakar (2008)
    <doi:10.18637/jss.v027.i03>. The method extends classical extrapolative
    forecasting approaches and is suited for operational and business planning
    contexts where stability and interpretability are important.