Create and maintain delayed-data packages (ddp's).  Data stored in
  a ddp are available on demand, but do not take up memory until requested.
  You attach a ddp with g.data.attach(), then read from it and assign to it in
  a manner similar to S-PLUS, except that you must run g.data.save() to
  actually commit to disk.

Ridge regression due to Hoerl and Kennard (1970)<DOI:10.1080/00401706.1970.10488634> and generalized ridge regression due to Yang and Emura (2017)<DOI:10.1080/03610918.2016.1193195> with optimized tuning parameters.
 These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors.
 Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.

Full descriptive statistics, physical description of sediment,
            metric or phi sieves.

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>.

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.