It gathers information, meta-data and scripts in a two-part Henry-Stewart talk by
 Zhao (2009, <doi:10.69645/DCRY5578>), which showcases analysis in aspects such as testing of polymorphic
 variant(s) for Hardy-Weinberg equilibrium, association with trait using genetic and statistical models
 as well as 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.

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.

The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method.
  By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed.
  This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others.
  First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user.
  By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions.
  The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem.