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.

Analysis of complex ANOVA models with any combination of orthogonal/nested and fixed/random factors, as described by Underwood (1997). There are two restrictions: (i) data must be balanced; (ii) fixed nested factors are not allowed. Homogeneity of variances is checked using Cochran's C test and 'a posteriori' comparisons of means are done using Student-Newman-Keuls (SNK) procedure. For those terms with no denominator in the F-ratio calculation, pooled mean squares and quasi F-ratios are provided. Magnitute of effects are assessed by components of variation.

Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).

A statistical ecosystem modelling package, taking many features of
             the ecosystem into account. Gadget works by running an internal
             model based on many parameters, and then comparing the data from
             the output of this model to real data to get a goodness-of-fit
             likelihood score. These parameters can then be adjusted, and the
             model re-run, until an optimum is found, which corresponds to the
             model with the lowest likelihood score. Gadget allows the user to
             include a number of features into an ecosystem model: One or more
             species, each of which may be split into multiple stocks; multiple
             areas with migration between areas; predation between and within
             species; maturation; reproduction and recruitment; multiple
             commercial and survey fleets taking catches from the populations.
             For more details see <https://gadget-framework.github.io/gadget2/>.
             This is the C++ Gadget2 runtime, making it available for R.